A real-time de-noising algorithm for e-noses in a wireless sensor network

Sensors

Volumn 9, Issue 2, 2009, Pages 895-908

  Qu, Jianfeng ^a   Chai, Yi ^a   Yang, Simon X ^b  

^a CHONGQING UNIVERSITY   (China)

^b UNIVERSITY OF GUELPH   (Canada)

Author keywords

Data analysis; Kalman filter; MOS gas sensor; Noise reduction

Indexed keywords

FILTER PARAMETER; KALMAN FILTER THEORY; KALMAN FILTERING TECHNIQUES; MEASUREMENT NOISE; METAL OXIDE SEMICONDUCTOR; MOS GAS SENSORS; NOISE CHARACTERISTIC; REAL-TIME DE-NOISING;

AGRICULTURE; DATA REDUCTION; GAS DETECTORS; KALMAN FILTERS; NOISE ABATEMENT; SPURIOUS SIGNAL NOISE; WIRELESS SENSOR NETWORKS;

CHEMICAL SENSORS;

EID: 63849253301     PISSN: 14248220     EISSN: None     Source Type: Journal    
DOI: 10.3390/s90200895     Document Type: Article

References (13)

1. Nawaf, A.K.; Jens, J.L.I. Classification of mixtures of odorants from livestock buildings by a sensor array (an electronic tongue). Sensors 2007, 7, 129-143.
2. Casalinuovo, I.A.; Pierro, D.D.; Coletta, M; Francesco, D.P. Application of electronic noses for disease diagnosis and food spoilage detection. Sensors 2006, 6, 1428-1439.
3. Pan, L.L.; Liu, R.; Peng, S.H.; Yang, S.X.; Gregori, S. Real-time monitoring system for odours around livestock farms. In Proceedings of the 2007 IEEE International Conference on Networking, Sensing and Control, London, 2007; pp.883-888.
4. Gao, D.Q.; Chen, W. Simultaneous estimation of odor classes and concentrations using an electronic nose with function approximation model ensembles. Sens. Actuat. 2007, 2, 584-594.
5. Szczurek, A.; Maciejewska, M. Relationship between odour intensity assessed by human assessor and TGS sensor array response. Sens. Actuat. B: Chem. 2005, 1, 13-19.
6. Wang, N.; Wang, M.H.; Zhang, N.Q. Wireless sensors in agriculture and food industry-recent development and future perspective. Comput. Electron. Agric. 2006, 1, 1-14.
7. Wang, L.; Su, S.W.; Celler, B.G.; Savkin, A.V. Modeling of a gas concentration measurement system. In Proceedings of the 27th Annual International Conference of the IEEE EMBS, Shanghai, 2005, pp. 6695 - 6698.
8. Tian, F.; Yang, S.X.; Dong, K. Circuit and noise analysis of odorant gas sensors in an e-nose. Sensors 2005, 5, 85-96.
9. Huang, Q.; Liu, J.; Li, H.W. A modified adaptive stochastic resonance for detecting faint signal in sensors. Sensors 2007, 7, 157-165.
10. Leleux, D.P.; Claps, R.; Chen, W.; Tittel, F.K.; Harman, T.L. Applications of Kalman filtering to real-time trace gas concentration measurements. Appl. Phys. B: Lasers Opt. 2002, 74, 85-93.
11. Olfati-Saber, R. Distributed Kalman filtering for sensor networks. In Proceedings of the 46th IEEE Conference on Decision and Control, New Orleans, 2007, pp. 5492 - 5498.
12. Hide, C.; Moore, T.; Smith, M. Adaptive Kalman filtering algorithms for integrating GPS and low cost INS. In Position Location and Navigation Symposium, 2004, pp. 227 - 233.
13. Pan, L.L.; Yang, S.X. An electronic nose network system for online monitoring livestock farm odors. IEEE Trans. Mechatron., In press.