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Related Concept Videos

Filtration00:53

Filtration

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Filtration is a physical separation process that involves passing a suspension through a porous medium to separate solids from fluids. During filtration, solids collect on the porous medium while liquids, also collectively known as the filtrate, pass through. The filtration medium is selected based on the filtration purpose, quantity, and nature of the precipitate. The general criteria for a suitable filtering medium are that it is inert, mechanically strong, nonabsorbent toward dissolved...
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Fineness Modulus01:19

Fineness Modulus

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The fineness modulus (FM) of aggregate is a numerical index that measures the coarseness or fineness of the particles. It is calculated by adding the cumulative percentages of aggregate retained on each of a specified series of sieves and dividing the sum by 100.
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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.
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Upsampling01:22

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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...
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Fast Fourier Transform01:10

Fast Fourier Transform

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The Fast Fourier Transform (FFT) is a computational algorithm designed to compute the Discrete Fourier Transform (DFT) efficiently. By breaking down the calculations into smaller, manageable sections, the FFT significantly reduces the computational complexity involved. Direct computation of an N-point DFT requires N2 complex multiplications, whereas the FFT algorithm needs only (N/2)log⁡2N multiplications, offering a much faster performance.
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Shape and Texture of Coarse Aggregate01:25

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Aggregate shape is classified based on the relative sharpness or roundness of the edges and corners. This classification includes categories like rounded, angular, elongated, and flaky, each with specific characteristics. Rounded aggregates, fully shaped by attrition, are typical of river or seashore gravel, while angular aggregates, such as crushed rock, have well-defined edges. Aggregates that are elongated and flaky are less desirable, as they can reduce the workability and strength of...
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Optical Scatter Microscopy Based on Two-Dimensional Gabor Filters
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Fine-Grained Hashing With Double Filtering.

Zhen-Duo Chen, Xin Luo, Yongxin Wang

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    Summary
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    Fine-grained hashing methods are advanced with FISH, a novel approach improving image retrieval. This method enhances feature extraction and refinement, achieving superior performance and faster convergence.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Fine-grained hashing is an emerging area in hashing-based retrieval, requiring further exploration.
    • Existing methods often struggle with simultaneous fine-grained feature extraction and refinement.

    Purpose of the Study:

    • To introduce a novel fine-grained hashing method, FISH (Fine-graIned haSHing), addressing key challenges in the field.
    • To improve the accuracy and efficiency of hashing-based image retrieval systems.

    Main Methods:

    • FISH utilizes a double-filtering mechanism, including Space Filtering for critical region highlighting and Feature Filtering for supervised feature refinement.
    • A proxy-based loss function is employed to train the model by preserving class-wise similarity relationships, enhancing efficiency and effectiveness.

    Main Results:

    • FISH demonstrates significantly improved retrieval performance compared to state-of-the-art fine-grained hashing techniques.
    • The proposed method exhibits rapid convergence during training, indicating high efficiency.

    Conclusions:

    • FISH offers a promising solution for fine-grained hashing, outperforming existing methods in retrieval accuracy.
    • The novel architecture and loss function contribute to both effectiveness and computational efficiency.