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Unsoundness in aggregates due to volume changes is primarily caused by the physical alterations aggregates undergo, such as freezing and thawing, thermal changes, and wetting and drying. Unsound aggregates, when subjected to these changes, result in volume change upon disintegration. This, in turn, contributes to the deterioration of concrete, including scaling, pop-outs, and cracking. Particular types of aggregates, such as porous flints, cherts, and those containing clay minerals, are...
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The human ear cannot distinguish between two sources of sound if they happen to reach within a specific time interval, typically 0.1 seconds apart. More than this, and they are perceived as separate sources.
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Aggregation Effects on Optimal Sensor Network Configurations with Distance-Dependent Noise.

Russell Costa1, Thomas A Wettergren1

  • 1Naval Undersea Warfare Center, Newport, RI 02841, USA.

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Summary
This summary is machine-generated.

Optimizing sensor placement for accurate source localization is key. Environmental noise and aggregation methods significantly impact sensor configuration, affecting different sensor types uniquely.

Keywords:
D-optimalityaggregationdistributed sensor networkenvironmental dependenceoptimal planningsensor configurationsource localization

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Area of Science:

  • Sensor networks
  • Signal processing
  • Optimization

Background:

  • Accurate source localization in sensor networks is vital.
  • Handling source location uncertainty often involves aggregation functions.
  • The combined effects of environmental noise and aggregation on sensor placement are understudied.

Purpose of the Study:

  • To investigate the interplay between environmental noise models and aggregation functions in sensor placement optimization.
  • To analyze how these factors influence optimal sensor configurations for different sensor types.

Main Methods:

  • Incorporated distance-dependent environmental noise models.
  • Analyzed sensor configurations using bearings-only and range-only sensors.
  • Developed computational strategies for optimal sensor placement.

Main Results:

  • Optimal sensor configuration strongly depends on the aggregation method when noise is considered.
  • This dependence varies significantly between different sensor types (e.g., bearings-only vs. range-only).

Conclusions:

  • Environmental complexity and aggregation approach are critical for robust sensor localization.
  • Careful consideration of both factors is necessary for designing effective sensor networks.