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Institution

Rensselaer Polytechnic Institute

EducationTroy, New York, United States
About: Rensselaer Polytechnic Institute is a education organization based out in Troy, New York, United States. It is known for research contribution in the topics: Terahertz radiation & Population. The organization has 19024 authors who have published 39922 publications receiving 1414699 citations. The organization is also known as: RPI & Rensselaer Institute.


Papers
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Journal ArticleDOI
TL;DR: This study suggests that diffusive instabilities should also be sought in ecological interactions by presenting an example of their occurrence when cooperating prey interact with predators.

622 citations

Journal ArticleDOI
TL;DR: The study of THz wave generation in the pulsed laser induced air plasma with individually controlled phase, polarization, and amplitude of the optical fundamental wave and its second harmonic indicates that the third-order nonlinear optical process mixing the omega and 2omega beams in the ionized plasma is the main mechanism of the efficient THZ wave generation.
Abstract: Our study of THz wave generation in the pulsed laser induced air plasma with individually controlled phase, polarization, and amplitude of the optical fundamental wave (omega) and its second harmonic (2omega) indicates that the third-order nonlinear optical process mixing the omega and 2omega beams in the ionized plasma is the main mechanism of the efficient THz wave generation. The polarity and the strength of the emitted THz field are completely controlled by the relative phase between the omega and 2omega waves. The measured THz field amplitude is proportional to the pulse energy of the fundamental beam and to the square root of the pulse energy of the second-harmonic beam once the total optical pulse energy exceeds the plasma formation threshold. The optimal THz field is achieved when all waves (omega, 2omega, and THz waves) are at the same polarization in the four-wave-mixing process.

619 citations

Journal ArticleDOI
01 Apr 1990
TL;DR: A compact representation of all possible assembly plans of a given product using AND/OR graphs is presented in this article, which forms the basis for efficient planning algorithms that make possible an increase in assembly system flexibility by allowing an intelligent robot to pick a course of action according to instantaneous conditions.
Abstract: A compact representation of all possible assembly plans of a given product using AND/OR graphs is presented. Such a representation forms the basis for efficient planning algorithms that make possible an increase in assembly system flexibility by allowing an intelligent robot to pick a course of action according to instantaneous conditions. The AND/OR graph is equivalent to a state transition graph but requires fewer nodes and simplifies the search for feasible plans. Three applications are discussed: the preselection of the best assembly plan, the recovery from execution errors, and the opportunistic scheduling of tasks. A hypothetical error situation in the assembly of the four-part assembly is discussed to show how a bottom-up search of the AND/OR graph leads to an efficient recovery. The scheduling efficiency using this representation is compared with fixed sequence and precedence graph representations. >

615 citations

Journal ArticleDOI
TL;DR: In this article, the authors quantitatively measured the relative early performance impact of this experience factor as well as the impact of several other experience and firm characteristics, including age, years of business, management, and technical experience.

608 citations

Journal ArticleDOI
TL;DR: It was concluded that reactive gliosis is an important part of the process forming the cellular sheath, at least partially composed of reactive glia, which isolates the probe from the brain.

605 citations


Authors

Showing all 19133 results

NameH-indexPapersCitations
Pulickel M. Ajayan1761223136241
Zhenan Bao169865106571
Murray F. Brennan16192597087
Ashok Kumar1515654164086
Joseph R. Ecker14838194860
Bruce E. Logan14059177351
Shih-Fu Chang13091772346
Michael G. Rossmann12159453409
Richard P. Van Duyne11640979671
Michael Lynch11242263461
Angel Rubio11093052731
Alan Campbell10968753463
Boris I. Yakobson10744345174
O. C. Zienkiewicz10745571204
John R. Reynolds10560750027
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202334
2022177
20211,118
20201,356
20191,328
20181,245