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Related Concept Videos

Area Computation by the Alternative Coordinate Method01:24

Area Computation by the Alternative Coordinate Method

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The alternative coordinate method, also known as the Shoelace Formula, is a technique for determining the area of a traverse using Cartesian coordinates. This method relies on the sequential arrangement of x and y coordinates for each point of the shape, ensuring accuracy and ease of application.In this approach, each corner's x and y coordinates are listed as fractions, with the x-coordinate as the numerator and the y-coordinate as the denominator. These coordinates are arranged sequentially...
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Updated: Mar 1, 2026

Simultaneously Capturing Real-time Images in Two Emission Channels Using a Dual Camera Emission Splitting System: Applications to Cell Adhesion
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Node Scheduling Strategies for Achieving Full-View Area Coverage in Camera Sensor Networks.

Peng-Fei Wu1,2, Fu Xiao3,4, Chao Sha5,6,7

  • 1School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210003, China. 2014070240@njupt.edu.cn.

Sensors (Basel, Switzerland)
|June 8, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces a novel model for full-view coverage using camera sensors, minimizing sensor numbers for complete region observation. Algorithms ensure efficient sensor deployment for optimal area coverage.

Keywords:
camera sensor networksfull-view area coveragesleep scheduling

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Area of Science:

  • Computer Science
  • Robotics
  • Sensor Networks

Background:

  • Conventional scalar sensors have limitations in capturing comprehensive object views.
  • Camera sensors offer multi-view capabilities, enabling novel coverage strategies.
  • Optimizing sensor placement is crucial for efficient surveillance and data acquisition.

Purpose of the Study:

  • To determine the minimum number of camera sensors required for full-view coverage of a region of interest (ROI).
  • To develop algorithms for sensor network deployment that guarantee full-view area coverage.
  • To address the challenge of sensor redundancy in random deployment scenarios.

Main Methods:

  • Derivation of constraint conditions for sensor positions ensuring full-view neighborhood coverage.
  • Proof that seamless stitching of regular hexagons from a virtual grid approximates full-view area coverage.
  • Development of two deployment strategies: Deployment Pattern Algorithm (DPA) for deterministic and Local Neighboring-Optimal Selection Algorithm (LNSA) for random deployments.

Main Results:

  • The study validates that hexagonal grid stitching ensures approximate full-view area coverage.
  • DPA provides an optimal solution for deterministic sensor network configurations.
  • LNSA effectively reduces redundancy in random deployments while achieving full-view coverage.

Conclusions:

  • The proposed full-view coverage model and algorithms are feasible for practical sensor network applications.
  • Efficient sensor selection and deployment are critical for maximizing coverage and minimizing resource utilization.
  • This research contributes to advancements in sensor network design for comprehensive environmental monitoring and surveillance.