Morphologic analysis based on statistical atlases is novel and useful to characterize the Asian humerus. The humerus demonstrates gender-specific morphology. This unique approach provides information that is useful to the clinician and biomedical engineer, not only in the modification of current or design of future humeral implants, but also in the precise dynamic positioning of Asian-specific humeral implants to Asian patients. Our findings support the need for further development of humeral implants, curvilinear robotics, and the questioning of whether gender-specific devices are necessary.
Door lock provides numerous benefits and has become indispensable in daily life as it acts as a security guard to prevent burglars and protect home belonging safely. The unlock methods of existing door lock system widely use keys and thumbprint involve touching the object may cause the spread of COVID-19. In this paper, a computer vision based security door lock system using Raspberry Pi (called SAFE) is proposed. Haar-Cascade classifier is employed as face detection classifier, while Local Binary Pattern Histogram (LBPH) is proposed as face recognition classifier. Recognition result is processed based on the usage of user to provide insights of SAFE. The accuracy of SAFE using pre-trained LBPH classifier achieves average of 86% based on the data obtained. The recognition speed outperforms existing work using principal component analysis and eigenfaces.
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