The healthcare sector has experienced significant technological advances; however, interoperability is one of the biggest challenges. Interoperability in healthcare refers to the capacity to communicate across different healthcare environments. The format, language, syntax, and interpretation of data differ from one healthcare setting to another. Therefore, the lack of interoperability hampers effective communication and data exchange between two healthcare settings. Following the introduction of the Internet of Things (IoT) in healthcare, document-level interoperability is no longer the sole concern; device-level interoperability is also critical. This paper introduces a new Sign Description Framework for healthcare IoT called Healthcare Sign Description Framework (HSDF). Three different signs in healthcare, namely the Vital sign, Medication sign, and Symptom sign, are discussed here. Our proposal demonstrates how interoperability can be achieved using the novel healthcare sign description framework. Implementation of this framework will lead to improved diagnosis and increased cost-effectiveness of treatment.
Human fingerprints be affluent in particulars called minutiae, which be able to used as credentials marks for fingerprint authentication. The objective of this project is to build up a complete system for fingerprint verification through extracting and matching minutiae. To achieve excellent minutiae extraction in fingerprints with varying quality, pre-processing in form of image enhancement and binarization is first applied on fingerprints before they are evaluated. Several methods have been joint to build a minutia extractor and a minutia matcher. Minutia-marking with false minutiae removal methods are used in the work. An alignment based elastic matching algorithm has been urbanized for minutia matching. This algorithm is capable of finding the correspondences between input minutiae pattern and the stored template minutia pattern without resorting to comprehensive search. Performance of the developed system is carried out by using FFT and DCT, and the results are analyzed on a data base with fingerprints from different people
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.