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Related Concept Videos

Detection of Black Holes01:10

Detection of Black Holes

Although black holes were theoretically postulated in the 1920s, they remained outside the domain of observational astronomy until the 1970s.
Their closest cousins are neutron stars, which are composed almost entirely of neutrons packed against each other, making them extremely dense. A neutron star has the same mass as the Sun but its diameter is only a few kilometers. Therefore, the escape velocity from their surface is close to the speed of light.
Not until the 1960s, when the first neutron...
Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
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...
IR Spectrum Peak Splitting: Symmetric vs Asymmetric Vibrations01:08

IR Spectrum Peak Splitting: Symmetric vs Asymmetric Vibrations

Identical bonds within a polyatomic group can stretch symmetrically (in-phase) or asymmetrically (out-of-phase). Similar to hydrogen bonding, these vibrations also influence the shape of the IR peak. Generally, asymmetric stretching frequencies are higher than symmetric stretching frequencies. For example, primary amines exhibit two distinct IR peaks between 3300–3500 cm−1 corresponding to the symmetric and asymmetric N-H stretching, while secondary amines exhibit a single stretching vibration...
¹³C NMR: ¹H–¹³C Decoupling01:04

¹³C NMR: ¹H–¹³C Decoupling

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Related Experiment Video

Updated: Jul 6, 2026

Wide-field Single-photon Optical Recording in Brain Slices Using Voltage-sensitive Dye
06:43

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Published on: June 20, 2019

Blockwise data detection for spectral hole-burning memories.

L Zhang, M A Neifeld

    Applied Optics
    |March 22, 2008
    PubMed
    Summary
    This summary is machine-generated.

    A new iterative log-likelihood (ILL) algorithm enhances data retrieval from spectral hole-burning (SHB) memory. This method reliably detects corrupted signals, significantly boosting SHB storage capacity by up to 197% compared to simpler techniques.

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    Last Updated: Jul 6, 2026

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    Area of Science:

    • Optical data storage
    • Information theory
    • Signal processing

    Background:

    • Spectral hole-burning (SHB) memory offers high-density data storage potential.
    • Data retrieval from SHB systems is susceptible to noise and intersymbol interference.
    • Existing detection algorithms have limitations in mitigating these retrieval challenges.

    Purpose of the Study:

    • To introduce and evaluate a novel iterative log-likelihood (ILL) algorithm for data retrieval in SHB memory systems.
    • To assess the performance of the ILL algorithm against conventional methods in terms of bit-error rate.
    • To quantify the storage capacity improvement offered by the ILL algorithm.

    Main Methods:

    • Development of a blockwise iterative log-likelihood (ILL) algorithm tailored for SHB system characteristics.
    • Implementation of the ILL algorithm to mitigate time-varying intersymbol interference and detector shot noise.
    • Comparative analysis of the ILL algorithm against five other detection techniques: precompensator, simple threshold, adaptive threshold, simple Wiener filter, and adaptive Wiener filter.

    Main Results:

    • The ILL algorithm demonstrated superior performance in bit-error rate compared to all five conventional methods.
    • ILL effectively mitigates time-varying intersymbol interference and detector shot noise in SHB data retrieval.
    • A significant storage capacity gain of 197% was achieved using the ILL algorithm over simple thresholding in a typical SHB system.

    Conclusions:

    • The iterative log-likelihood (ILL) algorithm represents a significant advancement for reliable data retrieval in spectral hole-burning memory.
    • ILL's ability to overcome noise and interference enhances the practical storage capacity of SHB technology.
    • The proposed algorithm offers a promising solution for improving the performance and efficiency of optical data storage systems.