An Image Analysis for Designing an Optimal Stirrer in Metal Matrix Composites Manufacturing

Farooq Muhammad — 2025-01-11

The global market for aluminum-based composites, widely used in manufacturing and construction, is expected to grow significantly. However, enhancing the cost-to-performance ratio is essential to improving their commercial viability. Efficient mixing plays a critical role in many industrial and chemical applications. Stir casting is the leading method for producing aluminum alloy matrix composites, but achieving a uniform particle distribution remains a significant challenge. In this study, the optimal stirrer design was identified using image processing techniques to analyze the distribution of ceramic grains. The stirrer that achieved the most uniform grain distribution was selected, eliminating the need for destructive testing. The mechanical properties of the final products validated the accuracy of the image analysis results.

Enhanced Kidney Stone Detection and Classification Using SVM and LBP Features

Hawkar K. Hama — 2025-01-13

Nephrolithiasis is a scientific term that refers to kidney stones and means the formation of crystal concretions in the kidney. It is considered a widespread situation that affects millions of people worldwide. Those stones can cause serious discomfort to infected people, especially when they traverse the urinary system, although, the big stones may need a surgical intervention. Various systems are already in use to address kidney stones, including ultrasound imaging for detection, extracorporeal shock wave lithotripsy (ESWL) for non-invasive stone fragmentation, and ureteroscopy for surgical removal, showcasing the advances in medical technology for managing this condition. This study presents an approach for detecting stones in the affected kidney. A public dataset has been employed in this work, containing (2370) images of healthy and affected kidneys. The dataset was utilized to train the proposed approach for the aim of stone detection. To achieve high detection accuracy, we implemented two key phases before classification. The preprocessing phase enhances image quality by reducing noise using a median filter and improving contrast through contrast stretching and tone enhancement. The segmentation phase follows, accurately identifying the kidney’s edges and regions of interest for effective feature extraction. The Local Binary Pattern (LBP) technique, combined with the support vector machine (SVM) algorithm serves as the primary components of the proposed model. The feature extraction comes into action through the LBP technique as a preparation step for the SVM classifier to complete the stone detection process. The approach introduced in this paper has the potential to enhance detection accuracy and efficiency. Furthermore, it could be used as an early detection tool to identify potential cases, thereby helping to prevent complications and adverse outcomes. This method aims to improve on the traditional manual process employed by radiologists, which could be described as time and effort consumption rather than the exposure of the interpretations. The obtained results were compared with the most relevant approaches in the field of kidney stone detection, demonstrating the model’s effectiveness in achieving the desired goal with a diagnostic accuracy of 96.37% for kidney stones.

UHD Journal of Science and Technology

An Image Analysis for Designing an Optimal Stirrer in Metal Matrix Composites Manufacturing

Enhanced Kidney Stone Detection and Classification Using SVM and LBP Features