RESUMEN
The recent development of micro-fabrication technologies has provided new methods for researchers to design and fabricate micro metal coils, which will allow the coils to be smaller, lighter, and have higher performance than traditional coils. As functional components of electromagnetic equipment, micro metal coils are widely used in micro-transformers, solenoid valves, relays, electromagnetic energy collection systems, and flexible wearable devices. Due to the high integration of components and the requirements of miniaturization, the preparation of micro metal coils has received increasing levels of attention. This paper discusses the typical structural types of micro metal coils, which are mainly divided into planar coils and three-dimensional coils, and the characteristics of the different structures of coils. The specific preparation materials are also summarized, which provides a reference for the preparation process of micro metal coils, including the macro-fabrication method, MEMS (Micro-Electro-Mechanical System) processing technology, the printing process, and other manufacturing technologies. Finally, perspectives on the remaining challenges and open opportunities are provided to help with future research, the development of the Internet of Things (IoTs), and engineering applications.
RESUMEN
A novel clustering method is proposed for mammographic mass segmentation on extracted regions of interest (ROIs) by using deterministic annealing incorporating circular shape function (DACF). The objective function reported in this study uses both intensity and spatial shape information, and the dominant dissimilarity measure is controlled by two weighting parameters. As a result, pixels having similar intensity information but located in different regions can be differentiated. Experimental results shows that, by using DACF, the mass segmentation results in digitized mammograms are improved with optimal mass boundaries, less number of noisy patches, and computational efficiency. An average probability of segmentation error of 7.18% for well-defined masses (or 8.06% for ill-defined masses) was obtained by using DACF on MiniMIAS database, with 5.86% (or 5.55%) and 6.14% (or 5.27%) improvements as compared to the standard DA and fuzzy c-means methods.
