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

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.

Impulsive Noise Cancellation from ECG Signal using Adaptive Filters and their Comparison

<em>Impulsive Noise is the sudden burst noise of short duration. Mostly it causes by electronic devices and electrosurgical noise in biomedical signals at the time of acquisition. In this work, Electrocardiograph (ECG) signal is considered and tried to remove impulsive noise from it. Impulsive noise in ECG signal is random type of noise. The objective of this work is to remove the noise using different adaptive algorithms and comparison is made among those algorithms. Initially the impulsive noise in sinusoidal signal is synthesized and tested for different algorithms like LMS, NLMS, RLS and SSRLS. Further those algorithms are modified in a new way to weight variation. The proposed novel approach is applied in the corrupted ECG signal to remove the noise. The effectiveness of the proposed approach is verified for ECG signal with impulsive noise as compared to the traditional approaches as well as previously proposed approaches. Also the performance of our approach is validated by SNR computation. Significant improvement in SNR is achieved after removal of noise.</em>