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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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Accurate signal sampling and reconstruction are crucial in various signal-processing applications. A time-domain signal's spectrum can be revealed using its Fourier transform. When this signal is sampled at a specific frequency, it results in multiple scaled replicas of the original spectrum in the frequency domain. The spacing of these replicas is determined by the sampling frequency.
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In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
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Uniform Depth Channel Flow: Problem Solving01:18

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In designing and analyzing filters, resonant circuits, or circuit analysis at large, working with standard element values like 1 ohm, 1 henry, or 1 farad can be convenient before scaling these values to more realistic figures. This approach is widely utilized by not employing realistic element values in numerous examples and problems; it simplifies mastering circuit analysis through convenient component values. The complexity of calculations is thereby reduced, with the understanding that...
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Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
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Author Spotlight: A Stable Phantom Material for Optical and Acoustic Imaging
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Double-Scale Adaptive Transmission in Time-Varying Channel for Underwater Acoustic Sensor Networks.

Yi Cen1, Mingliu Liu1,2, Deshi Li1,2

  • 1Electronic Information School, Wuhan University, Wuhan 430072, China.

Sensors (Basel, Switzerland)
|April 3, 2021
PubMed
Summary

Underwater acoustic sensor networks (UASNs) face dynamic channels. This study proposes a double-scale adaptive transmission mechanism using k-nearest neighbor prediction to optimize energy efficiency for improved underwater communication.

Keywords:
adaptive transmissiondouble-scale channel estimationtime-varying communication channelunderwater acoustic sensor networks

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Area of Science:

  • * Underwater Acoustic Sensor Networks (UASNs)
  • * Wireless Communication Systems
  • * Environmental Dynamics

Background:

  • * Underwater acoustic channels are time-varying due to environmental factors like ocean currents and temperature.
  • * These variations often follow predictable patterns, offering opportunities for optimization.
  • * Limited computational and energy resources in underwater nodes necessitate efficient communication strategies.

Purpose of the Study:

  • * To develop an energy-efficient adaptive transmission mechanism for UASNs.
  • * To improve data transmission by predicting and adapting to dynamic channel states.
  • * To optimize modulation, coding, and transmission power for long-term efficiency.

Main Methods:

  • * Decomposing historical channel state data into large-scale and small-scale series.
  • * Employing a novel k-nearest neighbor search algorithm with a sliding window for channel state prediction.
  • * Designing an energy-efficient transmission algorithm incorporating a quantitative model of data transmission and buffer thresholds.

Main Results:

  • * The proposed mechanism adaptively determines transmission configurations based on predicted channel states.
  • * Numerical simulations demonstrate effective channel prediction and significant energy consumption reduction.
  • * The system achieves good performance with a moderate buffer length under varying channel conditions and data arrival rates.

Conclusions:

  • * The double-scale adaptive transmission mechanism enhances energy efficiency in UASNs.
  • * Accurate channel state prediction is crucial for optimizing underwater communication.
  • * The proposed approach offers a viable solution for energy-constrained underwater sensor networks.