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Institution

University of Waterloo

EducationWaterloo, Ontario, Canada
About: University of Waterloo is a education organization based out in Waterloo, Ontario, Canada. It is known for research contribution in the topics: Population & Poison control. The organization has 36093 authors who have published 93906 publications receiving 2948139 citations. The organization is also known as: UW & uwaterloo.


Papers
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Journal ArticleDOI
TL;DR: It is argued that cardinality-based feature models can be interpreted as a special class of context-free grammars, and a semantic interpretation is provided by assigning an appropriate semantics to the language recognized by the corresponding grammar.
Abstract: Feature modeling is an important approach to capture the commonalities and variabilities in system families and product lines. Cardinality-based feature modeling integrates a number of existing extensions of the original feature-modeling notation from Feature-Oriented Domain Analysis. Staged configuration is a process that allows the incremental configuration of cardinality-based feature models. It can be achieved by performing a step-wise specialization of the feature model. In this article, we argue that cardinality-based feature models can be interpreted as a special class of context-free grammars. We make this precise by specifying a translation from a feature model into a context-free grammar. Consequently, we provide a semantic interpretation for cardinality-based feature models by assigning an appropriate semantics to the language recognized by the corresponding grammar. Finally, we give an account on how feature model specialization can be formalized as transformations on the grammar equivalent of feature models. Copyright © 2005 John Wiley & Sons, Ltd.

630 citations

Journal ArticleDOI
TL;DR: An efficient quantum algorithm for simulating the evolution of a quantum state for a sparse Hamiltonian H over a given time t is presented in terms of a procedure for computing the matrix entries of H.
Abstract: We present an efficient quantum algorithm for simulating the evolution of a quantum state for a sparse Hamiltonian H over a given time t in terms of a procedure for computing the matrix entries of H. In particular, when H acts on n qubits, has at most a constant number of nonzero entries in each row/column, and ||H|| is bounded by a constant, we may select any positive integer k such that the simulation requires O((log*n)t1+1/2k) accesses to matrix entries of H. We also show that the temporal scaling cannot be significantly improved beyond this, because sublinear time scaling is not possible.

626 citations

Journal ArticleDOI
01 Oct 1996
TL;DR: This paper presents a complete design methodology for Cartesian position based visual servo control for robots with a single camera mounted at the end-effector and the implementation using a distributed computer architecture is described.
Abstract: This paper presents a complete design methodology for Cartesian position based visual servo control for robots with a single camera mounted at the end-effector. Position based visual servo control requires the explicit calculation of the relative position and orientation (POSE) of the workpiece object with respect to the camera. This is accomplished using image plane measurements of a number of known feature points on the object, and then applying an extended Kalman filter to obtain a recursive solution of the photogrammetric equations, and to properly combine redundant measurements. The control is then designed by specifying the desired trajectories with respect to the object and forming the control error in the end-effector frame. The implementation using a distributed computer architecture is described. An experimental system has been built and used to evaluate the performance of the POSE estimation and the position based visual servo control. Several results for relative trajectory control and target tracking are presented. Results of the experiments showing the effect of loss of some of the redundant features are also presented.

626 citations

Journal ArticleDOI
Kazunori Akiyama, Antxon Alberdi1, Walter Alef2, Keiichi Asada3  +243 moreInstitutions (60)
TL;DR: In this paper, the Event Horizon Telescope (EHT) 1.3 mm radio wavelength observations of the supermassive black hole candidate at the center of the radio galaxy M87 and the quasar 3C 279, taken during the 2017 April 5-11 observing campaign are presented.
Abstract: We present the calibration and reduction of Event Horizon Telescope (EHT) 1.3 mm radio wavelength observations of the supermassive black hole candidate at the center of the radio galaxy M87 and the quasar 3C 279, taken during the 2017 April 5–11 observing campaign. These global very long baseline interferometric observations include for the first time the highly sensitive Atacama Large Millimeter/submillimeter Array (ALMA); reaching an angular resolution of 25 μas, with characteristic sensitivity limits of ~1 mJy on baselines to ALMA and ~10 mJy on other baselines. The observations present challenges for existing data processing tools, arising from the rapid atmospheric phase fluctuations, wide recording bandwidth, and highly heterogeneous array. In response, we developed three independent pipelines for phase calibration and fringe detection, each tailored to the specific needs of the EHT. The final data products include calibrated total intensity amplitude and phase information. They are validated through a series of quality assurance tests that show consistency across pipelines and set limits on baseline systematic errors of 2% in amplitude and 1° in phase. The M87 data reveal the presence of two nulls in correlated flux density at ~3.4 and ~8.3 Gλ and temporal evolution in closure quantities, indicating intrinsic variability of compact structure on a timescale of days, or several light-crossing times for a few billion solar-mass black hole. These measurements provide the first opportunity to image horizon-scale structure in M87.

625 citations


Authors

Showing all 36498 results

NameH-indexPapersCitations
John J.V. McMurray1781389184502
David A. Weitz1781038114182
David Taylor131246993220
Lei Zhang130231286950
Will J. Percival12947387752
Trevor Hastie124412202592
Stephen Mann12066955008
Xuan Zhang119153065398
Mark A. Tarnopolsky11564442501
Qiang Yang112111771540
Wei Zhang112118993641
Hans-Peter Seidel112121351080
Theodore S. Rappaport11249068853
Robert C. Haddon11257752712
David Zhang111102755118
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
20241
2023213
2022701
20215,359
20205,388
20195,200