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Fuel Level Estimation in Tank of Truck in Motion

Biernacki Pawel, Libal Urszula

International Journal of Electronics and Telecommunications

2025

Vol. 71, No. 2

413-418

The paper presents the results of a case study on estimating the fuel level in the tank of a motor vehicle. A method based on the concept of particle filtering of noisy measurement data is proposed. The algorithm designed using the Sequential Monte Carlo method with Sequential Importance Sampling is combined with classical digital filters used for signal filtering. In the simulations, real data obtained by measuring fuel levels in the tanks of TIR heavy trucks from one of the Polish trucking companies are used. The performance of the applied method was considered in various measurement situations, such as refueling, driving on an uneven road surface, driving on steep roads, and fading of the measurement signals.

MFCC-Based Sound Classification of Honey Bees

Libal Urszula, Biernacki Pawel

International Journal of Electronics and Telecommunications

2024

Vol. 70, No. 4

849-853

Smart beekeeping is a rapidly developing field. Automated detection and classification of honey bees opens many new opportunities for studies on their behavior. In this paper, we focus on distinguishing between two classes of bees: female workers and male drones. The classification is performed on mel-frequency cepstral coefficients obtained for audio recordings of their flights in a close proximity to an entrance to a beehive. We compare the classification accuracy for several classifiers. We investigate how partitioning of the frequency spectrum influences the classification results. The study involves series of experiments performed for different cepstral representations in the form of 5, 10, 15, 20 and 40 mel-frequency cepstral coefficients.