Time-Frequency (t-f) distributions are frequently employed for analysis of new-born EEG signals because of their non-stationary characteristics. Most of the existing time-frequency distributions fail to concentrate energy for a multicomponent signal having multiple directions of energy distribution in the t-f domain. In order to analyse such signals, we propose an Adaptive Directional Time-Frequency Distribution (ADTFD). The ADTFD outperforms other adaptive kernel and fixed kernel TFDs in terms of its ability to achieve high resolution for EEG seizure signals. It is also shown that the ADTFD can be used to define new time-frequency features that can lead to better classification of EEG signals, e.g. the use of the ADTFD leads to 97.5% total accuracy, which is by 2% more than the results achieved by the other methods.

The red-banded stink bug, Piezodorus guildinii, is an important pest in soybean. Information on its distribution is needed to determine the most effective timing for pest control and strategies to avoid yield losses, such as adequate monitoring. The present study was aimed at examining the temporal variation and spatial distribution of P. guildinii in soybean. The experimental design comprised an area of 5400 m² planted with soybean, with 54 plots of 100 m² each, in which plants were examined weekly for nymphs and adults of P. guildinii with a beating sheet. Evaluations were carried out from soybean emergence to harvest; however, P. guildinii occurred only during the reproductive stage. Based on aggregation indices, theoretical frequency distributions, and semivariograms, nymphs and adults were randomly distributed at the beginning of infestation but, tended to be aggregate during pod setting and seed filling. Our findings have a contribution to improving pest sampling systems and infestation mapping, including future semiochemicals studies.

The most crucial transmission components utilized in rotating machinery are gears and bearings. In a gearbox, the bearings support the force acting on the gears. Compound Faults in both the gears and bearings may cause heavy vibration and lead to early failure of components. Despite their importance, these compound faults are rarely studied since the vibration signals of the compound fault system are strongly dominated by noise. This work proposes an intelligent approach to fault identification of a compound gear-bearing system using a novel Bessel kernel-based Time-Frequency Distribution (TFD) called the Bessel transform. The Time-frequency images extracted using the Bessel transform are used as an input to the Convolutional Neural Network (CNN), which classifies the faults. The effectiveness of the proposed approach is validated with a case study, and a testing efficiency of 94% is achieved. Further, the proposed method is compared with the other TFDs and found to be effective.