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What are Estimates?01:06

What are Estimates?

8.2K
It isn't easy to measure a parameter such as the mean height or the mean weight of a population. So, we draw samples from the population and calculate the mean height or mean weight of the individuals in the sample. This sample data acts as a representative measure of the population parameter. These sample statistics are known as estimates. 
The estimate for the mean of a sample is denoted by ͞x, whereas the mean of the population is designated as μ. Further, parameters such...
8.2K
Estimation of k and VD of Aminoglycosides01:20

Estimation of k and VD of Aminoglycosides

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Aminoglycosides are a class of antibiotics used to treat various bacterial infections. Clinicians must determine the elimination rate constant (k) and volume of distribution (VD) to optimize therapeutic efficacy and minimize toxicity. The k value represents the rate at which the drug is removed from the body, and the VD reflects the degree to which the drug distributes into body tissues. Accurately estimating these parameters allows healthcare professionals to tailor drug dosing to individual...
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Estimation of the Physical Quantities01:05

Estimation of the Physical Quantities

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On many occasions, physicists, other scientists, and engineers need to make estimates of a particular quantity. These are sometimes referred to as guesstimates, order-of-magnitude approximations, back-of-the-envelope calculations, or Fermi calculations. The physicist Enrico Fermi was famous for his ability to estimate various kinds of data with surprising precision. Estimating does not mean guessing a number or a formula at random. Instead, estimation means using prior experience and sound...
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NMR Spectroscopy: Chemical Shift Overview01:15

NMR Spectroscopy: Chemical Shift Overview

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The position of the absorption signal of a sample is reported relative to the position of the signal of tetramethylsilane (TMS), which is added as an internal reference while recording spectra. The difference between the absorption frequencies of the sample and TMS (in Hz) is divided by the spectrometer operating frequency (in MHz) to obtain a dimensionless quantity called the chemical shift. It is reported on the δ (delta) scale and expressed in parts per million.
For instance, the proton...
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Proton (¹H) NMR: Chemical Shift01:07

Proton (¹H) NMR: Chemical Shift

3.3K
Organic molecules primarily contain carbon and hydrogen atoms. While all the hydrogen isotopes are NMR-active, protium or hydrogen-1 is the most abundant. It has a significant energy separation between its nuclear spin states due to its large gyromagnetic ratio. As per Boltzmann's distribution, an increase in the energy separation implies a greater excess population of nuclei available for excitation, resulting in a strong NMR absorption signal.
Absorption signals of all the protium nuclei...
3.3K
Estimating Population Mean with Known Standard Deviation01:16

Estimating Population Mean with Known Standard Deviation

9.6K
To construct a confidence interval for a single unknown population mean μ, where the population standard deviation is known, we need sample mean as an estimate for μ and we need the margin of error. Here, the margin of error (EBM) is called the error bound for a population mean (abbreviated EBM). The sample mean is the point estimate of the unknown population mean μ.
The confidence interval estimate will have the form as follows:
(point estimate - error bound, point estimate +...
9.6K

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Related Experiment Video

Updated: Jan 24, 2026

Lensfree On-chip Tomographic Microscopy Employing Multi-angle Illumination and Pixel Super-resolution
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Improving sub-pixel shifts estimation in a Shack-Hartmann wavefront sensor.

Popowicz Adam

    Optics Letters
    |May 16, 2019
    PubMed
    Summary

    A new algorithm enhances Shack-Hartmann (SH) wavefront sensor accuracy by analyzing relative sub-aperture displacements. This method improves image registration quality, especially for rapidly changing scenes without a reference image.

    Area of Science:

    • Optics and Photonics
    • Astronomy and Astrophysics
    • Image Processing

    Background:

    • Accurate wavefront sensing is crucial for adaptive optics and imaging systems.
    • Traditional Shack-Hartmann (SH) sensors rely on measuring shifts relative to a reference image, which can be problematic for dynamic scenes.

    Purpose of the Study:

    • To develop a novel algorithm for improving the accuracy of displacement determination in SH wavefront sensor sub-apertures.
    • To provide a robust method for wavefront sensing in scenarios where a high-fidelity reference image is unavailable or rapidly changing.

    Main Methods:

    • The proposed method transforms a matrix of all relative sub-aperture displacements into a vector of final shifts.
    • This approach avoids direct measurement against a single reference image, enhancing robustness.

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    Main Results:

    • The algorithm significantly increases the accuracy of estimated shifts between sub-aperture images.
    • It effectively reduces nonlinearities and biases inherent in existing SH wavefront sensing techniques.
    • The method demonstrates applicability to dynamic scenes where reference images are difficult to obtain.

    Conclusions:

    • The novel algorithm offers a substantial improvement in SH wavefront sensing accuracy and reliability.
    • This technique is particularly valuable for astronomical observations and other applications with rapidly evolving targets.