In aviation, transportation is a direct connection between the fuel consumed by aircrafts and carbon dioxide emissions. A new solution for determination of the necessary aircraft fuel will lead to companies’ savings and environment protection. The proposed method considers the commercial passenger aviation. The software developed by International Civil Aviation (ICAO) use a methodology for simulation of necessary fuel and for computing carbon emissions. A part of this methodology uses fixed value for passenger’s weight determination. Not all passengers from a flight have the same weight and, thus, the proposed method could replace the fixed value. Based on mathematical morphology operators, the new algorithm will improve the accuracy of aircraft fuel consumption with significant results on environmental protection.
This paper demonstrates the fabrication of a smart sleeve, able to monitor the elbow flexion by means of the carrying angle. The setup of the system includes the use of a polymeric optical fiber (POF) sensor, i.e., a sensor based on a POF fiber, a light-emitting diode (LED) and a photodiode. This represents an element of novelty in medicine, where until now only mobile applications or costly devices have been used for obtaining the desired results. The purpose of our paper is to replace the classical goniometer, used by physicians, with an optical goniometer, much easier to use, that the patient can use at home, without being necessary to go to a clinic. Moreover, our proposed application is unique, as it also belongs to the domain of smart textile devices. The goniometer is embedded in a sleeve that the patient can wear anytime and anywhere, to find the carrying angle, a highly important parameter for sportsmen, who often fracture their arms during their activities. The information obtained from the measurements can offer the patient an evaluation of his health state or of his degree of recovery after a fracture or operation, without requiring a visit to a physiotherapist.
Face detection has multiple applications including recognition, people identification and detection of facial expressions. With the current pandemic crisis and due to the measures imposed to prevent Covid-19 spreading, the wearing of a protection mask became mandatory. The object of interest of this paper is to detect the wearing of an approved mask face using Viola Jones algorithm, aggregate channel features (ACF) and mathematical morphology. COVID-19 virus spread through the air, so it is necessary that all the materials used for manufacturing of the face masks to filter properly the air, and only the approved face masks to be used in order to control the spread infection. The algorithm used for face detection is Viola Jones with notable success in real time face detection and real time impression speed in face detection. Identification of the approved face mask against Covid-19 virus is made with a trained ACF detector. Eye detection, necessary to check if the face mask is properly placed, is based on mathematical morphology operators. These operations used together are robust with high results on the image processing.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.