Baza IEEE Xplore indeksuje publikacje pokonferencyjne z konferencji międzynarodowych, organizowanych pod patronatem IEEE. W 2020 roku pracownicy KMiSD wzięli udział w dwóch takich konferencjach, czego efektem są publikacje:
1. Hanus R., Zych M., Chorzępa R., Golijanek-Jędrzejczyk A.: Investigations of the methods of time delay measurement of stochastic signals using cross-correlation with the Hilbert Transform. 2020 IEEE 20th Mediterranean Electrotechnical Conference (MELECON). (https://ieeexplore.ieee.org/document/9140630)
Abstract
The article presents the results of simulation studies of four methods of time delay estimation for random signals using cross-correlation with the Hilbert Transform. Selected models of mutually delayed stochastic signals were used in the simulations, corresponding to the signals obtained from scintillation detectors in radioisotope measurements of liquid-gas two-phase flow. Standard deviations of the values of the individual functions were designated and compared, along with standard deviations of time delay estimates determined on their basis. The obtained results were compared with the results for classic cross-correlation function (CCF). It was found that for the analysed range of the signal-to-noise ratio (SNR): 0.2 ≤ SNR ≤ 5, the lowest values of standard deviation of time delay estimates were obtained for the CCFHT function (cross-correlation with the Hilbert Transform of the delayed signal).
2. Wilk B., Augustyn M., Wilk G.: Algorithm for human fall detection based on acceleration measurement. 2020 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA). (https://ieeexplore.ieee.org/document/9241243)
Abstract
According to the World Health Organization, a fall is defined as an unexpected event in which participant comes to rest on the ground, floor, or lower level. Falls are one of the most serious life-threatening events. Automatic detection of a fall can reduce the time of an arrival of medical attention and consequences of prolonged lying after a fall.In this paper, a novel algorithm is presented for a human fall detection based on acceleration measurement using the 3-axis sensor placed in the pocket. This algorithm was tested on two data sets with simulated falls and various daily activities. The obtained results show that the proposed algorithm allows us to achieve both sensitivity of 93% and specificity of 94.5% at the same time. These are values much higher than currently reported in the literature.
