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E-Nose, a new way of sensing Nevrus Kaja Electrical and Computer Engineering Department University of Michigan - Dearborn, Michigan, USA nkaja@umich.edu. Introduction. Conclusion. Architecture.

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  1. E-Nose, a new way of sensing Nevrus Kaja Electrical and Computer Engineering Department University of Michigan - Dearborn, Michigan, USA nkaja@umich.edu Introduction Conclusion Architecture • Developing devices with functionality similar to the human nose can influence advances in a number of important applications. • An E-noseis a prototype of a sensing device composed of an array of chemical sensors which produces digital signals when complexes from its near-by range are recognized. • The aim of this work is related to: • Development of a unique, reliable and standard e-nose sensor by combining research from two major fields (chemoreceptor and embedded design) • Elimination of mechanical pumps and encouraging the use of thin-film, metal-oxide gas sensors • Motivation: • The lack of a defining standard for the e-nose sensors • Existing research mainly perform in the area of odor differentiation Adding more sensors to the sensor array is the most important part of the e-nose architecture The algorithm was developed to provide the best possible variable view of a multivariable data set to improve the performance of the identifier The scope of this research is to develop a multipurpose standard product The bases for a collaborative effort and establishment of a global control entity were set Future work • Figure 2. Proposed E-nose architecture Figure 1. Example of a chemical input sensor array used in e-nose Replacing the layer of chemical sensors in a timely manner Using filtered signal as an output for the analysis done in the chemical array Providing a larger database for more chemicals to better assist the identification • In order to reduce the e-nose and expand the range of substances that can be recognized, a replaceable chemical sheet (layer) is proposed as shown in Figure 3. Approach • By studying the environment and technologies for detecting similar substance, new sources of inspiration for e-nose development were found: • Dog sense of smell • An examination of how dogs are able to differentiate objects through the sense of smell was performed. • The findings indicate that object differentiation is performed by the olfactory mucus layer which adsorbs and concentrates the air molecules. • Gas detection technology • It is based on the detection of the concentration of a specific gas in the air and referencing it to a point or scale • Different types of sensors are used for the chemical inputs sensor array (Figure 1) such as catalytic, infrared, electrochemical and metal oxide semiconductor. References Results • Figure 3. Proposed replaceable chemical sensor array • [1] Kaja, Nevrus. Lubna, Alazawi. Raishouni, Ghanem. “E-Nose, developing a standard product” - International Symposium in Engineering and Natural Science. Macau – China, August 2013 • [2] Optoelectronic and Microelectronic Materials and Devices., Author: Eisele, I. , Page(s): 285 – 291. • [3] Michael Griffin, “E- NOSES: MULTI-SENSOR ARRAYS”, Davidson College, NC, 28036 • [4] G Pioggia1,” A processing architecture for associative short-term memory in e- noses”, Interdepartmental Research Center „E Piaggio‟, Faculty of Engineering, University of Pisa, via Diotisalvi 2, Pisa, Italy To achieve and evaluate different results in the analysis of odors in an e-nose, we are using two dimensional PCA ( Principal Component Analysis). Our PCA model is designed to provide the best possible view of variability in the independent variables of a multivariable data set. It converts a set of possible correlated variables into a set of linearly uncorrelated variables. These variables are called principle components, which usually have a number less than or equal to the number of original variables (Figure 4). Using PCA graphics, we are able to construct the relation between different odors and compare the results that we get from the sensors with the ones that we already know . • Figure 4. Example of a PCA Graph

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