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Region of Convergence of Laplace Tarnsform01:20

Region of Convergence of Laplace Tarnsform

The Region of Convergence (ROC) is a fundamental concept in signal processing and system analysis, particularly associated with the Laplace transform. The ROC represents an area in the complex plane where the Laplace transform of a given signal converges, determining the transform's applicability and utility.
Consider a decaying exponential signal that begins at a specific time. When deriving its Laplace transform, the time-domain variable is replaced with a complex variable. This substitution...
Region of Convergence01:17

Region of Convergence

The z-transform is a powerful mathematical tool used in the analysis of discrete-time signals and systems. It is a crucial tool in the analysis of discrete-time systems, but its convergence is limited to specific values of the complex variable z. This range of values, known as the Region of Convergence (ROC), is fundamental in determining the behavior and stability of a system or signal. The ROC defines the region in the complex plane where the z-transform converges, which can take various...
IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the C=O, C=N, and C=C occur between 1600–1850 cm−1.
The...

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Updated: Jun 22, 2026

Magnetic Resonance Derived Myocardial Strain Assessment Using Feature Tracking
07:21

Magnetic Resonance Derived Myocardial Strain Assessment Using Feature Tracking

Published on: February 12, 2011

Source region identification using kernel smoothing.

Ronald Henry1, Gary A Norris, Ram Vedantham

  • 1Department of Civil and Environmental Engineering, University of Southern California, 3620 S. Vermont Ave., Los Angeles, California 90089-2531, USA. rhenry@usc.edu

Environmental Science & Technology
|July 3, 2009
PubMed
Summary
This summary is machine-generated.

Nonparametric wind regression identified pollution sources impacting SO2 levels in East St. Louis. A zinc smelter and brewery were significant contributors, while a steel mill showed minimal impact.

Related Experiment Videos

Last Updated: Jun 22, 2026

Magnetic Resonance Derived Myocardial Strain Assessment Using Feature Tracking
07:21

Magnetic Resonance Derived Myocardial Strain Assessment Using Feature Tracking

Published on: February 12, 2011

Area of Science:

  • Environmental Science
  • Atmospheric Chemistry
  • Statistical Modeling

Background:

  • Source apportionment models are crucial for identifying pollutant origins.
  • Understanding the impact of industrial sources on local air quality is essential for public health.
  • Previous methods may lack the precision to differentiate contributions from specific wind sectors.

Purpose of the Study:

  • To introduce and detail a nonparametric wind regression model for source apportionment.
  • To apply this model to quantify SO2 contributions from different wind direction sectors in East St. Louis.
  • To assess the model's uncertainty using both analytical formulas and bootstrap methods.

Main Methods:

  • Utilized nonparametric kernel smoothing for pollutant apportionment based on wind data.
  • Defined source regions using wind direction and speed sectors.
  • Calculated model component uncertainties and compared them with blocked bootstrap estimates.

Main Results:

  • The nonparametric wind regression model successfully apportioned SO2 concentrations.
  • Two 30-degree wind sectors, associated with a zinc smelter and a brewery, accounted for nearly 50% of the average SO2.
  • A nearby steel mill demonstrated no significant impact on SO2 levels during the study period.

Conclusions:

  • Nonparametric wind regression is an effective tool for source-to-receptor apportionment.
  • Industrial facilities like smelters and breweries can be significant local sources of SO2.
  • The model provides reliable uncertainty estimates, enhancing its applicability in air quality management.