Processing and Prospect of Electronic Nose

Open Access

Issue		E3S Web Conf. Volume 271, 2021 2021 2^nd International Academic Conference on Energy Conservation, Environmental Protection and Energy Science (ICEPE 2021)


Article Number		01038
Number of page(s)		6
Section		Energy Development and Utilization and Energy Storage Technology Application
DOI		https://doi.org/10.1051/e3sconf/202127101038
Published online		15 June 2021

V. H. Tran, H. P. Chan, M. Thurston, P. Jackson, C. Lewis, D. Yates, G. Bell, P. S. Thomas. Breat Analysis of Lung Cancer Patients Using an Electronik Nose Detection System. IEEE Sensors Journal, Vol. 10, No. 9, 2010. [Google Scholar]
E. J. Lewintre, M. Uso, E. Sanmartin, C. Camps. Update on Biomarkers for the Detection of Lung Cancer. Dove Press Journal, 3 (2012), pp. 21–29, 2012. [Google Scholar]
Ü. Özsandikcioglu, A. Atasoy and S. Yapici. Hybrid Sensor Based E-Nose For Lung Cancer Diagnosis. In: 2018 IEEE International Symposium on Medical Measurements and Applications (MeMeA), Rome, Italy, 2018, pp. 1–5, DOI: 10.1109/MeMeA.2018.8438596 [Google Scholar]
G. Peng, U. Tisch, O. Adams, M. Hakim, N. Shehada, Y.Y. Broza, S. Billan, R. Abdah-Bortnyak, A. Kuten, H. Haick. Diagnosing lung cancer in exhaled breath using gold nanoparticles. Nature Nanotechnology, 2009, 4(10), 669–673. [CrossRef] [PubMed] [Google Scholar]
Y. Liu, Q. Meng, X. Zhang. Data Processing for Multiple Electronic Noses Using Sensor Response Visualization. In: IEEE Sensors Journal, 2018, vol. 18, no. 22, pp. 9360–9369, 15 Nov. [CrossRef] [Google Scholar]
R. Hegde and S. Seema. Performance Evaluation and Feature Vector Extraction of Review Data Sets Using Evolutionary Based SVM Techniques. In: 2018 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT), Bangalore, 2018, pp. 1–4. [Google Scholar]
J. Suriya Prakash, K. Annamalai Vignesh, C. Ashok and R. Adithyan. Multi class Support Vector Machines classifier for machine vision application. In: 2012 International Conference on Machine Vision and Image Processing (MVIP), Taipei, 2012, pp. 197–199. [CrossRef] [Google Scholar]
F. Reverter, D. Gomez and J. Altet. On-Chip MOSFET Temperature Sensor for Electrical Characterization of RF Circuits. In: IEEE Sensors Journal, 2013, vol. 13, no. 9, pp. 3343–3344. [CrossRef] [Google Scholar]
G. C. Frye, et al. Optimizing surface acoustic wave sensors for trace chemical detection. In: Proceedings of International Solid State Sensors and Actuators Conference (Transducers ’97), Chicago, IL, USA, 1997, vol. 2, pp. 1323–1326. [CrossRef] [Google Scholar]
W. Kam, W.S. Mohammed, S. O’Keeffe and E. Lewis. Portable 3-D Printed Plastic Optical Fibre Motion Sensor for Monitoring of Breathing Pattern and Respiratory Rate. In: 2019 IEEE 5th World Forum on Internet of Things (WF-IoT), Limerick, Ireland, 2019, pp. 144–148. [CrossRef] [Google Scholar]
Y. Takei, Y. Kanazawa, K. Hirasawa and H. Nanto. Development of 3D gas source localization using multi-copter with gas sensor array. In: 2019 IEEE International Symposium on Olfaction and Electronic Nose (IsOeN), Fukuoka, Japan, 2019, pp. 1–4. [Google Scholar]
Ü. Özsandikcroglu, S. Yapici and A. Atasoy. Classification of Different Breath Samples Utilizing E-Nose System. In: 2018 41st International Conference on Telecommunications and Signal Processing (TSP), Athens, Greece, 2018, pp. 1–5, DOI: 10.1109/TSP.2018.8441254. [Google Scholar]
T. Zeng, H. Xu, X. Huang. The progress, challenge and future of exhaled breath sensor. Chinese Journal of Scientific Instrument, 2019, 40 (08): 65–81. [Google Scholar]
L. Zhou, Z.H. Wang, D.C. Sun, et al. Present situation and development of modern precision measurement technology. Chinese Journal of Scientific Instrument. 2017, 38(8): 1869–1878. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.