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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Answer: • Visible Light, Radio Waves
• Radio - 305-m , at Arecibo, Puerto Rico
Visible Light - 10.4m Mirror, Canary Island
Explanation:
The spectral window is simply the range of frequencies that are correctly measured. It should be noted that the signals that are outside the spectral window are folded when they show up in spectrum.
The two spectral windows through which electromagnetic radiation easily reaches the surface of earth are the visible light and the radio waves.
they have more energy than radio waves.
&
because the wavelength of the light waves are too small
Answer:
Explanation:
There's a formula for this:

F being force, k being the spring constant, and displacement being the change in x
We are given the force and the spring constant, so this is essentially isolating the Δx term. Do 60N/120N per meter. The newtons cancel out and you get a final answer of Δx = 0.5 meters