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    A new segmented fast linear canonical transform handles large data efficiently. This method segments data to maintain frequency resolution and precision, outperforming existing transforms.

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    Area of Science:

    • Signal Processing
    • Optics
    • Applied Mathematics

    Background:

    • Discrete and fast linear canonical transforms are key research areas in signal processing and optics.
    • Handling large datasets with limited memory for linear canonical frequency spectrum analysis is a significant challenge.

    Purpose of the Study:

    • To propose a novel segmented fast linear canonical transform algorithm.
    • To address the problem of processing very large input data with limited memory while maintaining frequency resolution.

    Main Methods:

    • The proposed method segments large input data into smaller, manageable segments.
    • Algorithm complexity is analyzed for various signal types.
    • Performance is evaluated against existing fast linear canonical transforms regarding resolution and precision.

    Main Results:

    • The segmented approach enables the calculation of very large datasets.
    • The method successfully maintains ideal frequency resolution.
    • Experimental results validate the algorithm's correctness and performance.

    Conclusions:

    • The segmented fast linear canonical transform is an effective solution for processing large data in memory-constrained environments.
    • The proposed algorithm offers comparable or superior resolution and precision to existing methods.
    • This work contributes a practical approach to linear canonical transform analysis for large-scale data.