Next Article in Journal
Cadmium-Sensitive Measurement Using a Nano-Copper-Enhanced Carbon Fiber Electrode
Previous Article in Journal
Design and Validation of a Holographic Particle Counter
 
 
Article

Adaptive-Cognitive Kalman Filter and Neural Network for an Upgraded Nondispersive Thermopile Device to Detect and Analyze Fusarium Spores

by and *
Department of Electrical and Computer Engineering, University of Saskatchewan, Saskatoon, SK S7N 5A9, Canada
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(22), 4900; https://doi.org/10.3390/s19224900
Received: 11 October 2019 / Revised: 5 November 2019 / Accepted: 6 November 2019 / Published: 9 November 2019
(This article belongs to the Section Biosensors)
Noises such as thermal noise, background noise or burst noise can reduce the reliability and confidence of measurement devices. In this work, a recursive and adaptive Kalman filter is proposed to detect and process burst noise or outliers and thermal noise, which are popular in electrical and electronic devices. The Kalman filter and neural network are used to preprocess data of three detectors of a nondispersive thermopile device, which is used to detect and quantify Fusarium spores. The detectors are broadband (1 µm to 20 µm), λ 1 (6.09 ± 0.06 µm) and λ 2 (9.49 ± 0.44 µm) thermopiles. Additionally, an artificial neural network (NN) is applied to process background noise effects. The adaptive and cognitive Kalman Filter helps to improve the training time of the neural network and the absolute error of the thermopile data. Without applying the Kalman filter for λ 1 thermopile, it took 12 min 09 s to train the NN and reach the absolute error of 2.7453 × 104 (n. u.). With the Kalman filter, it took 46 s to train the NN to reach the absolute error of 1.4374 × 104 (n. u.) for λ 1 thermopile. Similarly, to the λ 2 (9.49 ± 0.44 µm) thermopile, the training improved from 9 min 13 s to 1 min and the absolute error of 2.3999 × 105 (n. u.) to the absolute error of 1.76485 × 105 (n. u.) respectively. The three-thermopile system has proven that it can improve the reliability in detection of Fusarium spores by adding the broadband thermopile. The method developed in this work can be employed for devices that encounter similar noise problems. View Full-Text
Keywords: burst noise; outlier; thermal noise; Kalman; filter; neural network; thermopile; Fusarium detection burst noise; outlier; thermal noise; Kalman; filter; neural network; thermopile; Fusarium detection
Show Figures

Graphical abstract

MDPI and ACS Style

Pham, S.; Dinh, A. Adaptive-Cognitive Kalman Filter and Neural Network for an Upgraded Nondispersive Thermopile Device to Detect and Analyze Fusarium Spores. Sensors 2019, 19, 4900. https://doi.org/10.3390/s19224900

AMA Style

Pham S, Dinh A. Adaptive-Cognitive Kalman Filter and Neural Network for an Upgraded Nondispersive Thermopile Device to Detect and Analyze Fusarium Spores. Sensors. 2019; 19(22):4900. https://doi.org/10.3390/s19224900

Chicago/Turabian Style

Pham, Son, and Anh Dinh. 2019. "Adaptive-Cognitive Kalman Filter and Neural Network for an Upgraded Nondispersive Thermopile Device to Detect and Analyze Fusarium Spores" Sensors 19, no. 22: 4900. https://doi.org/10.3390/s19224900

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop