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Related Concept Videos

BIBO stability of continuous and discrete -time systems01:24

BIBO stability of continuous and discrete -time systems

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System stability is a fundamental concept in signal processing, often assessed using convolution. For a system to be considered bounded-input bounded-output (BIBO) stable, any bounded input signal must produce a bounded output signal. A bounded input signal is one where the modulus does not exceed a certain constant at any point in time.
To determine the BIBO stability, the convolution integral is utilized when a bounded continuous-time input is applied to a Linear Time-Invariant (LTI) system....
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Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

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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.
In the...
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Discrete Fourier Transform01:15

Discrete Fourier Transform

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The Discrete Fourier Transform (DFT) is a fundamental tool in signal processing, extending the discrete-time Fourier transform by evaluating discrete signals at uniformly spaced frequency intervals. This transformation converts a finite sequence of time-domain samples into frequency components, each representing complex sinusoids ordered by frequency. The DFT translates these sequences into the frequency domain, effectively indicating the magnitude and phase of each frequency component present...
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Discrete-time Fourier transform01:26

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The Discrete-Time Fourier Transform (DTFT) is an essential mathematical tool for analyzing discrete-time signals, converting them from the time domain to the frequency domain. This transformation allows for examining the frequency components of discrete signals, providing insights into their spectral characteristics. In the DTFT, the continuous integral used in the continuous-time Fourier transform is replaced by a summation to accommodate the discrete nature of the signal.
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Factors Influencing Drug Absorption: Physicochemical Parameters01:22

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The physicochemical characteristics of drugs play a crucial role in formulating stable and bioavailable drug products. The solubility of a drug, governed by the varying pH along the GI tract and its dissociation constant (pKa), is pivotal in determining its ionization state and absorption rate. Notably, weak acids and bases remain unionized and are absorbed more rapidly.
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Basic Discrete Time Signals01:16

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The unit step sequence is defined as 1 for zero and positive values of the integer n. This sequence can be graphically displayed using a set of eight sample points, showing a step function starting from n=0 and remaining constant thereafter.
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Protocol for Microplastics Sampling on the Sea Surface and Sample Analysis
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Profiling physicochemical and planktonic features from discretely/continuously sampled surface water.

Azusa Oita1, Yuuri Tsuboi1, Yasuhiro Date2

  • 1RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan.

The Science of the Total Environment
|April 28, 2018
PubMed
Summary

This study introduces an integrated strategy using machine learning and other analytical methods to assess surface water quality. The approach reveals distinct features of bay water, including seasonal metabolite variations and microalgae correlations.

Keywords:
Machine learningMetabolomeMicrobiomeMulti-omicsMultidisciplinary approachesVector autoregressive model

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Area of Science:

  • Environmental Science
  • Ecology
  • Analytical Chemistry

Background:

  • Aquatic ecosystems face global endangerment, necessitating advanced assessment methods.
  • Understanding complex aquatic ecosystems requires integrating multiple factors using omics technologies.

Purpose of the Study:

  • To propose an integrated analytical strategy for extracting surface water features.
  • To apply this strategy to analyze bay water characteristics in Odaiba, Tokyo.

Main Methods:

  • An integrated strategy combining machine learning, factor mapping, and forecast-error-variance decomposition.
  • Analysis of datasets including ions, metabolites, and microorganisms.
  • Application to 681 surface water samples from Japan.

Main Results:

  • Machine learning clustered surface water samples, identifying Odaiba water as low in inorganic ions (Mg, Ba, B).
  • Factor mapping showed Odaiba water is rich in metabolites and poor in ions during summer.
  • Forecast-error-variance decomposition revealed correlations between microalgae (Raphidophyceae) and specific metabolites, ions, and wind speed.

Conclusions:

  • The integrated strategy effectively analyzes biological, chemical, and physical factors in surface water.
  • This approach aids in understanding and assessing endangered aquatic ecosystems.