Browsing by Subject "Number of peoples"

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    ItemOpen Access
    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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    ItemOpen Access
    A robust system for counting people using an infrared sensor and a camera
    (Elsevier BV, 2015) Erden, F.; Alkar, A. Z.; Çetin, A. Enis
    In this paper, a multi-modal solution to the people counting problem in a given area is described. The multi-modal system consists of a differential pyro-electric infrared (PIR) sensor and a camera. Faces in the surveillance area are detected by the camera with the aim of counting people using cascaded AdaBoost classifiers. Due to the imprecise results produced by the camera-only system, an additional differential PIR sensor is integrated to the camera. Two types of human motion: (i) entry to and exit from the surveillance area and (ii) ordinary activities in that area are distinguished by the PIR sensor using a Markovian decision algorithm. The wavelet transform of the continuous-time real-valued signal received from the PIR sensor circuit is used for feature extraction from the sensor signal. Wavelet parameters are then fed to a set of Markov models representing the two motion classes. The affiliation of a test signal is decided as the class of the model yielding higher probability. People counting results produced by the camera are then corrected by utilizing the additional information obtained from the PIR sensor signal analysis. With the proof of concept built, it is shown that the multi-modal system can reduce false alarms of the camera-only system and determines the number of people watching a TV set in a more robust manner.