Stretchable One-Dimensional Conductors for Wearable Applications.

Continuous, one-dimensional (1D) stretchable conductors have attracted significant attention for the development of wearables and soft-matter electronics. Through the use of advanced spinning, printing, and textile technologies, 1D stretchable conductors in the forms of fibers, wires, and yarns can be designed and engineered to meet the demanding requirements for different wearable applications. Several crucial parameters, such as microarchitecture, conductivity, stretchability, and scalability, play essential roles in designing and developing wearable devices and intelligent textiles. Methodologies and fabrication processes have successfully realized 1D conductors that are highly conductive, strong, lightweight, stretchable, and conformable and can be readily integrated with common fabrics and soft matter. This review summarizes the latest advances in continuous, 1D stretchable conductors and emphasizes recent developments in materials, methodologies, fabrication processes, and strategies geared toward applications in electrical interconnects, mechanical sensors, actuators, and heaters. This review classifies 1D conductors into three categories on the basis of their electrical responses: (1) rigid 1D conductors, (2) piezoresistive 1D conductors, and (3) resistance-stable 1D conductors. This review also evaluates the present challenges in these areas and presents perspectives for improving the performance of stretchable 1D conductors for wearable textile and flexible electronic applications.

End-member extraction based on segmented vertex component analysis in hyperspectral images.

Hyperspectral images collected by a remote sensing hyperspectral imaging instrument have many mixed pixels, due to the limited resolution of image sensors and the complex diversity of nature. End-member extraction is the process that determines the end-members in mixed pixels. The results of traditional methods are inaccurate, due to the spatial complexity and noise of actual hyperspectral image data. This study presents segmented vertex component analysis (SVCA), wherein the relative complexities of hyperspectral images are segmented into a number of relatively simple spatial subsets to reduce the effect of uncorrelated pixels. The end-members are extracted by finding the vertices of the simplex that minimally encloses the hyperspectral image data in each spatial subset, and the inversion abundance is used to identify each major end-member in each subset. Experimental results demonstrate that the proposed method can effectively implement end-member extraction with high accuracy.