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Related Concept Videos

Shrinkage in Concrete01:27

Shrinkage in Concrete

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Shrinkage in concrete is primarily due to water loss from evaporation, hydration of cement, or carbonation, leading to a reduction in volume. The volumetric contraction results in volumetric strain in concrete. However, in practice, shrinkage is measured as linear strain, which is one-third of the volumetric strain.
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When hardened concrete is exposed to air with a relative humidity of less than 100 percent, it begins to lose the free water within its capillaries. As this water evaporates, the water initially adsorbed onto the calcium silicate hydrates migrates towards these now empty spaces and eventually evaporates as well. Over time, as more water leaves, the volume of the concrete decreases, a phenomenon known as drying shrinkage.
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Atmospheric CO2 penetrates the concrete's pores and, in the presence of moisture, forms carbonic acid, which then reacts with calcium hydroxide in the hydrated cement, forming calcium carbonate. This process reduces the concrete's volume and is termed carbonation shrinkage.
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Sound Source Localization Testing in Single-sided Deafness Following Bone Conduction Intervention
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A sparse loudspeaker array for surround sound reproduction using the least absolute shrinkage and selection operator

Yuchen Shen1, Hongyi Zhu1, Yanye Yang1

  • 1Key Laboratory of Modern Acoustics (Ministry of Education), Department of Physics, Nanjing University, Nanjing 210093, China121120106@smail.nju.edu.cn, mg1722100@smail.nju.edu.cn, dz1622045@smail.nju.edu.cn, yshen@nju.edu.cn.

The Journal of the Acoustical Society of America
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Summary
This summary is machine-generated.

A new beamforming algorithm uses the Lasso method for sparse speaker arrays. This approach improves surround sound reproduction by creating narrower beams and reducing side lobes, allowing for user-adjustable settings.

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Area of Science:

  • Acoustics
  • Signal Processing
  • Audio Engineering

Background:

  • Multi-channel surround sound systems often require complex and costly speaker arrays.
  • Traditional beamforming methods can struggle with achieving narrow beams and low side lobes simultaneously.

Purpose of the Study:

  • To develop a cost-effective beamforming algorithm for sparse speaker arrays.
  • To enhance the performance of multi-channel surround sound reproduction.

Main Methods:

  • Utilizing a least absolute shrinkage and selection operator (Lasso)-based algorithm.
  • Applying the algorithm to a sparse cylindrically baffled speaker array.
  • Evaluating performance through simulation and experimental validation.

Main Results:

  • The Lasso-based beamforming algorithm achieved a narrower beamwidth compared to existing methods.
  • A significant reduction in side lobe levels was observed.
  • The algorithm demonstrated effectiveness in both simulated and real-world scenarios.

Conclusions:

  • The proposed Lasso-based beamforming offers a superior and adaptable solution for low-cost surround sound.
  • Sparsity in speaker arrays, controlled by the regularization parameter, allows for user-defined adjustments.
  • This method provides a practical approach to enhance audio reproduction quality.