Browsing by Subject "Sensing mechanism"

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Open Access
Binary coded identification of industrial chemical vapors with an optofluidic nose
(OSA - The Optical Society, 2016) Adamu, A. I.; Ozturk, F. E.; Bayındır, Mehmet
An artificial nose system for the recognition and classification of gas-phase analytes and its application in identifying common industrial gases is reported. The sensing mechanism of the device comprises the measurement of infrared absorption of volatile analytes inside the hollow cores of optofluidic Bragg fibers. An array of six fibers is used, where each fiber targets a different region of the mid-infrared in the range of 2-14 ìm with transmission bandwidths of about 1-3 μm. The quenching in the transmission of each fiber due to the presence of analyte molecules in the hollow core is measured separately and the cross response of the array allows the identification of virtually any volatile organic compound (VOC). The device was used for the identification of seven industrial VOC vapors with high selectivity using a standard blackbody source and an infrared detector. The array response is registered as a unique six digit binary code for each analyte by assigning a threshold value to the fiber transmissions. The developed prototype is a comprehensive and versatile artificial nose that is applicable to a wide range of analytes.
Open Access
Grating coupler integrated photodiodes for plasmon resonance based sensing in fluidic systems
(IEEE, 2011) Türker, Burak; Güner, Hasan; Ayaş, Sencer; Ekiz, Okan O.; Acar, Handan; Güler, Mustafa O.; Dâna, Aykutlu
We demonstrate an integrated sensor combining a grating-coupled plasmon resonance surface with a planar photodiode. Plasmon enhanced transmission is employed as a sensitive refractive index (RI) sensing mechanism and monitored via the integrated photodiode. © 2011 OSA.
Open Access
Identification of materials with magnetic characteristics by neural networks
(2012) Nazlibilek, S.; Ege, Y.; Kalender O.; Sensoy, M.G.; Karacor, D.; Sazli, M.H.
In industry, there is a need for remote sensing and autonomous method for the identification of the ferromagnetic materials used. The system is desired to have the characteristics of improved accuracy and low power consumption. It must also autonomous and fast enough for the decision. In this work, the details of inaccurate and low power remote sensing mechanism and autonomous identification system are given. The remote sensing mechanism utilizes KMZ51 anisotropic magneto-resistive sensor with high sensitivity and low power consumption. The images and most appropriate mathematical curves and formulas for the magnetic anomalies created by the magnetic materials are obtained by 2-D motion of the sensor over the material. The contribution of the paper is the use of the images obtained by the measurement of the perpendicular component of the Earth magnetic field that is a new method for the purpose of identification of an unknown magnetic material. The identification system is based on two kinds of neural network structures. The MultiLayer Perceptron (MLP) and the Radial Basis Function (RBF) network types are used for training of the neural networks. In this work, 23 different materials such as SAE/AISI 1030, 1035, 1040, 1060, 4140 and 8260 are identified. Besides the ferromagnetic materials, three objects are also successfully identified. Two of them are anti-personal and anti-tank mines and one is an empty can box. It is shown that the identification system can also be used as a buried mine identification system. The neural networks are trained with images which are originally obtained by the remote sensing system and the system is operated by images with added Gaussian white noises. © 2012 Elsevier Ltd. All rights reserved.