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Related Concept Videos

Aliasing01:18

Aliasing

717
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.
If the sampling frequency is below the Nyquist rate, these replicas overlap, preventing the original...
717
Upsampling01:22

Upsampling

673
Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
673
Downsampling01:20

Downsampling

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When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
731
2D NMR: Heteronuclear Single-Quantum Correlation Spectroscopy (HSQC)01:19

2D NMR: Heteronuclear Single-Quantum Correlation Spectroscopy (HSQC)

1.5K
Heteronuclear single-quantum correlation spectroscopy (HSQC) is a 2D NMR technique that reveals one-bond correlations between hydrogen and a heteronucleus. The HSQC experiment is similar to the heteronuclear correlation experiment (HETCOR) but is more sensitive. In the HSQC spectrum, the proton chemical shift is plotted on the horizontal F2 axis, while the 13C chemical shift is plotted on the vertical F1 axis. The corresponding proton and 13C spectra are also shown. The HSQC contour plot does...
1.5K
IR Spectrum Peak Splitting: Symmetric vs Asymmetric Vibrations01:08

IR Spectrum Peak Splitting: Symmetric vs Asymmetric Vibrations

1.9K
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...
1.9K
¹H NMR: Interpreting Distorted and Overlapping Signals01:02

¹H NMR: Interpreting Distorted and Overlapping Signals

1.6K
Spin systems where the difference in chemical shifts of the coupled nuclei is greater than ten times J are called first-order spin systems. These nuclei are weakly coupled, and their chemical shifts and coupling constant can generally be estimated from the well-separated signals in the spectrum.
As Δν decreases and the signals move closer, the doublets appear increasingly distorted. The intensities of the inner lines increase at the cost of those of the outer lines as the signals are...
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Reversed Spectral Hashing.

Qingshan Liu, Guangcan Liu, Lai Li

    IEEE Transactions on Neural Networks and Learning Systems
    |May 11, 2017
    PubMed
    Summary
    This summary is machine-generated.

    Reversed spectral hashing (ReSH) improves unsupervised hashing by interchanging inputs and outputs, ensuring similar data points map to adjacent codes and dissimilar ones are separated. This method achieves state-of-the-art retrieval results.

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    Area of Science:

    • Computer Science
    • Machine Learning
    • Information Retrieval

    Background:

    • Spectral hashing (SH) is a classical unsupervised hashing method for efficient large-scale search indices.
    • SH aims to preserve data similarity structure in low-dimensional hash codes but often yields unsatisfactory performance.
    • The inferior performance of SH stems from its imperfect formulation, failing to guarantee similarity preservation in the hash code space.

    Purpose of the Study:

    • To address the limitations of spectral hashing (SH) in preserving data similarity.
    • To introduce a novel hashing technique, reversed spectral hashing (ReSH), that overcomes SH's drawbacks.
    • To achieve state-of-the-art retrieval performance through an improved hashing formulation.

    Main Methods:

    • Introduced reversed spectral hashing (ReSH), interchanging the input and output of traditional SH.
    • Defined data point similarities based on unknown low-dimensional hash codes, unlike SH's data-driven approach.
    • Solved the ReSH minimization problem using multilayer neural networks.

    Main Results:

    • ReSH ensures similar data points are mapped to adjacent hash codes.
    • ReSH effectively separates dissimilar data points in the code space.
    • Achieved state-of-the-art retrieval results on three benchmark datasets.

    Conclusions:

    • Reversed spectral hashing (ReSH) offers a robust solution for unsupervised hashing by redefining the similarity preservation mechanism.
    • The novel formulation of ReSH guarantees better separation of dissimilar data points, enhancing retrieval accuracy.
    • ReSH, optimized via neural networks, represents a significant advancement in large-scale search system indexing.