Improved method for prediction of milled rice moisture content based on Weibull distribution

In order to explore the use of side-polished fibre (SPF) for microprobe-type "lab-on-fibre", this study presents an analysis of the surface roughness in side-polished fiber (SPF) using the gray level co-occurrence matrix (GLCM) texture feature analysis method. Experimental results show that the flat areas of the SPF polished surface exhibit texture characteristics with high mean values in contrast and entropy, and low mean values in the angular second moment (ASM), homogeneity, and correlation. Employing the random forest (RF) feature importance ranking method based on the Gini coefficient and out-of-bag (OOB) error estimation, this study assesses the sensitivity of various GLCM texture parameters in classifying different roughness levels of the SPF polished surfaces. A feature subset comprising variance, ASM, entropy, and contrast is identified as optimal. Utilizing this subset, the paper conducts an RF classification validation experiment on the roughness of the SPF polished surfaces, with results showing an RF classification accuracy of 95.65%.This research provides evidence for exploring the impact of rough polished surfaces in SPF optic sensors on the light coupling mechanism with environmental materials and its influence on sensor sensitivity. It lays the foundation for exploring the precise identification of high-sensitivity areas on SPF polished surfaces.

Underwater image enhancement is a crucial task for marine exploration and computer vision applications. However, existing methods often suffer from severe quality degradations resulting from light absorption and scattering, color distortion, contrast reduction, and detail loss. This paper introduces FST-former, a novel Transformer-based framework that integrates Fourier modelling and sparse attention mechanisms to address these issues. Specifically, the proposed method includes a Multi-Branch Fourier Fusion Module that enhances multi-scale feature extraction capability in both frequency and spatial domains, ensuring effective enhancement of degraded regions. In addition, a Frequency-Adaptive Sparse Attention module is introduced to induce attention sparsity and refine key information fusion, thereby reducing interference from irrelevant regions. Extensive experiments on several public underwater datasets demonstrate that the proposed FST-former achieves superior performance compared with existing advanced approaches in terms of both restoration quality and model efficiency, achieving up to a 21.6576 PSNR and 0.8564 SSIM on the Test-L500 dataset.