A State Observer-Based Fractional-Order Approach for Trajectory Tracking and Attitude Control of A Quadrotor

Bist, Ashutosh; Sondhi, Swati

doi:10.1080/03772063.2023.2175054

Cited by 2 publications

(1 citation statement)

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“…[29] FO Sobel Some researches were applied for image contrast enhancement using fractional order masks in [30]. For instance, based on GL definition, a fractional-order mask was presented, and when followed by a Sobel edge detector, the algorithm was able to detect the details of low contrast images with better sharpness and higher accuracy than the traditional integer-order masks [31]. The proposed algorithm in such work enhanced the high-frequency components in the images while preserving the low-frequency ones.…”

Section: Survey On Fractional-order Image Edge Detectionmentioning

confidence: 99%

Fractional-Order Edge Detection Masks for Diabetic Retinopathy Diagnosis as a Case Study

et al. 2021

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Edge detection is one of the main steps in the image processing field, especially in biomedical imaging, to diagnose a disease or trace its progress. The transfer of medical images makes them more susceptible to quality degradation due to any imposed noise. Hence, the protection of this data against noise is a persistent need. The efficiency of fractional-order filters to detect fine details and their high noise robustness, unlike the integer-order filters, it renders them an attractive solution for biomedical edge detection. In this work, two novel central fractional-order masks are proposed with their detailed mathematical proofs. The fractional-order parameter gives an extra degree of freedom in designing different masks. The noise performance of the proposed masks is evaluated upon applying Salt and Pepper noise and Gaussian noise. Numerical results proved that the proposed masks outperform the integer-order masks regarding both types of noise, achieving higher Peak Signal to Noise Ratio. As a practical application, the proposed fractional-order edge detection masks are employed to enhance the Diabetic Retinopathy disease diagnosis.

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Section: Survey On Fractional-order Image Edge Detectionmentioning

confidence: 99%