Multiple-Source Shortest Paths in Embedded Graphs

We give a subquadratic (O(n3/2+∊) time and O(n) space) algorithm for computing Euclidean shortest paths in the plane in the presence of polygonal obstacles; previous time bounds were at least quadratic in n, in the worst case. The method avoids use of visibility graphs, relying instead on the continuous Dijkstra paradigm. The output is a shortest path map (of size O(n)) with respect to a given source point, which allows shortest path length queries to be answered in time O( log n). The algorithm extends to the case of multiple source points, yielding a method to compute a Voronoi diagram with respect to the shortest path metric.

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A Simple Algorithm for Multiple-Source Shortest Paths in Planar Digraphs

10.1137/1.9781611977066.1 ◽

2022 ◽

pp. 1-11

Author(s):

Debarati Das ◽

Evangelos Kipouridis ◽

Maximilian Probst Gutenberg ◽

Christian Wulff-Nilsen

Keyword(s):

Shortest Paths ◽

Simple Algorithm ◽

Multiple Source

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Multiple-source Record Linkage in a Rural Industrial Community, 1680–1820

History and Computing ◽

10.3366/hac.1994.6.3.133 ◽

1994 ◽

Vol 6 (3) ◽

pp. 133-142 ◽

Cited By ~ 7

Author(s):

Steve King

Keyword(s):

Early Modern ◽

Large Scale ◽

Social Economic ◽

Life Cycles ◽

Multiple Source ◽

Demographic Analysis ◽

Ordinary People ◽

Industrial Community ◽

The Social ◽

Broad Outline

Re-creating the social, economic and demographic life-cycles of ordinary people is one way in which historians might engage with the complex continuities and changes which underlay the development of early modern communities. Little, however, has been written on the ways in which historians might deploy computers, rather than card indexes, to the task of identifying such life cycles from the jumble of the sources generated by local and national administration. This article suggests that multiple-source linkage is central to historical and demographic analysis, and reviews, in broad outline, some of the procedures adopted in a study which aims at large scale life cycle reconstruction.

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Evaluation of options for improved large grid multiple-source schlieren systems

10.2514/6.1994-2301 ◽

1994 ◽

Cited By ~ 2

Author(s):

S. Collicott

Keyword(s):

Multiple Source ◽

Large Grid

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eMap: A Web Application for Identifying and Visualizing Electron or Hole Hopping Pathways in Proteins

10.26434/chemrxiv.7889993 ◽

2019 ◽

Author(s):

Ruslan N. Tazhigulov ◽

James R. Gayvert ◽

Melissa Wei ◽

Ksenia B. Bravaya

Keyword(s):

Web Application ◽

Shortest Paths ◽

Coarse Grained ◽

Decay Parameter ◽

Electron Hole ◽

Web Based ◽

Aromatic Amino Acid Residue ◽

Pathways Model ◽

Visualization Tools ◽

Effective Decay

<p>eMap is a web-based platform for identifying and visualizing electron or hole transfer pathways in proteins based on their crystal structures. The underlying model can be viewed as a coarse-grained version of the Pathways model, where each tunneling step between hopping sites represented by electron transfer active (ETA) moieties is described with one eﬀective decay parameter that describes protein-mediated tunneling. ETA moieties include aromatic amino acid residue side chains and aromatic fragments of cofactors that are automatically detected, and, in addition, electron/hole residing sites that can be speciﬁed by the users. The software searches for the shortest paths connecting the user-speciﬁed electron/hole source to either all surface-exposed ETA residues or to the user-speciﬁed target. The identiﬁed pathways are ranked based on their length. The pathways are visualized in 2D as a graph, in which each node represents an ETA site, and in 3D using available protein visualization tools. Here, we present the capability and user interface of eMap 1.0, which is available at https://emap.bu.edu.</p>

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Implementation of a single-channel active noise control system with multiple reference sensors

Noise Control Engineering Journal ◽

10.3397/1/376830 ◽

2020 ◽

Vol 68 (5) ◽

pp. 358-366

Author(s):

H.E. Oh ◽

W.B. Jeong ◽

C. Hong

Keyword(s):

Single Channel ◽

Control Method ◽

Reference Signal ◽

Active Noise Control ◽

Multiple Source ◽

Weighted Sum ◽

Reference Signals ◽

Feedforward Controller ◽

Channel Control ◽

Source Signals

When multiple sources contribute competitively to the noise level, multi-channel control architecture is needed, leading to more cost and time for control computation. We, hence, are concerned with a single-channel control method with a single-reference signal obtained from a linear combination of the multiple source signals. First, we selected 3 source signal sensors for the reference signals and the error sensor, selected a proper actuator and designed the controllers: 3 cases of single-channel feedforward controllers with a single-reference signal respectively from the source signals, a multi-channel feedforward controller with the reference signals from the source signals, and the proposed controller with the reference signal from weighted sum of the source signals. The weighting factors and the filter coefficients of the controller were determined by the FxLMS algorithm. An experiment was then performed to confirm the effectiveness of the proposed method comparing the control performance with other methods for a tower air conditioner. The overall sound pressure level (SPL) detected by the error sensor is compared to evaluate their performance. The reduction in the overall SPL was obtained by 4.74 dB, 1.96 dB and 6.62 dB, respectively, when using each of the 3 reference signals. Also, the overall SPL was reduced by 7.12 dB when using the multi-reference controller and by 7.66 dB when using the proposed controller. Conclusively, under the multiple source contribution, a single-channel feed forward controller with the reference signal from a weighted sum of the source signals works well with lower cost than multi-channel feedforward controller.

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Computer algorithms

10.1093/oso/9780198805090.003.0008 ◽

2018 ◽

Author(s):

Mark Newman

Keyword(s):

Data Structures ◽

Analysis Of Algorithms ◽

Shortest Paths ◽

Minimum Cut ◽

Network Data ◽

Computer Algorithms ◽

Breadth First Search ◽

Augmenting Path ◽

Basic Concepts ◽

Maximum Flows

This chapter introduces some of the fundamental concepts of numerical network calculations. The chapter starts with a discussion of basic concepts of computational complexity and data structures for storing network data, then progresses to the description and analysis of algorithms for a range of network calculations: breadth-first search and its use for calculating shortest paths, shortest distances, components, closeness, and betweenness; Dijkstra's algorithm for shortest paths and distances on weighted networks; and the augmenting path algorithm for calculating maximum flows, minimum cut sets, and independent paths in networks.

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