Browsing by Subject "Sensor applications"

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    Glucose sensors based on electrospun nanofibers: a review
    (Springer Verlag, 2016) Senthamizhan, A.; Balusamy, B.; Uyar, Tamer
    The worldwide increase in the number of people suffering from diabetes has been the driving force for the development of glucose sensors. The recent past has devised various approaches to formulate glucose sensors using various nanostructure materials. This review presents a combined survey of these various approaches, with emphasis on the current progress in the use of electrospun nanofibers and their composites. Outstanding characteristics of electrospun nanofibers, including high surface area, porosity, flexibility, cost effectiveness, and portable nature, make them a good choice for sensor applications. Particularly, their nature of possessing a high surface area makes them the right fit for large immobilization sites, resulting in increased interaction with analytes. Thus, these electrospun nanofiber-based glucose sensors present a number of advantages, including increased life time, which is greatly needed for practical applications. Taking all these facts into consideration, we have highlighted the latest significant developments in the field of glucose sensors across diverse approaches.
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    RadGT: graph and transformer-based automotive radar point cloud segmentation
    (Institute of Electrical and Electronics Engineers, 2023-10-25) Sevimli, R. A.; Ucuncu, M.; Koç, Aykut
    The need for visual perception systems providing situational awareness to autonomous vehicles has grown significantly. While traditional deep neural networks are effective for solving 2-D Euclidean problems, point cloud analysis, particularly for radar data, contains unique challenges because of the irregular geometry of point clouds. This letter proposes a novel transformer-based architecture for radar point clouds adapted to the graph signal processing (GSP) framework, designed to handle non-Euclidean and irregular signal structures. We provide experimental results by using well-established benchmarks on the nuScenes and RadarScenes datasets to validate our proposed method.