Source localization in ocean acoustics is posed as a machine learning problem in which data-driven methods learn source ranges directly from observed acoustic data: True.
<h3>What is machine learning?</h3>
Machine learning (ML) is also known as artificial intelligence (AI) and it can be defined as a subfield in computer science which typically focuses on the use of computer algorithms, data-driven techniques (methods) and technologies to develop a smart computer-controlled robot that has the ability to automatically perform and manage tasks that are exclusively meant for humans or solved by using human intelligence.
In Machine learning (ML), data-driven techniques (methods) are used to learn source ranges directly from observed acoustic data in a bid to proffer solutions to source localization in ocean acoustics.
In conclusion, a normalized sample covariance matrix (SCM) is constructed and used as the input, especially after pre-processing the pressure that's received by a vertical linear array in Machine learning (ML).
Read more on machine learning here: brainly.com/question/25523571
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Core
Home of atoms of hydrogen also the lightest element in the universe.
Radiative Zone
Outside the inner Core it radiates energy through the process of photon emission.
Convection Layer
Outer most Layer of the Core, it extends form a depth of 200,000 kilometres to the visible surface. Energy is created by Convection. This is where light is produced.
Photosphere
Surrounds the stars and is where light and heat radiate.
Chromosphere
Reddish gas layer outside of the photosphere I think it also works with the Corona.
Corona
Aura of Plasma that surrounds the Sun and other stars, it extends millions of kilometres and easily seen during a total eclipse.
