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A method for solving the instantaneous mixtures of the multiple non-stationary wideband signals in the timefrequency (TF) plane is proposed. The blind source separation is performed by calculation of spatial TF distribution matrices, estimation of a separating matrix, estimation of permutation matrices and scaling matrices and TF synthesis. The simulation result shows that the proposed method improves the signal-to-distortion ratio than the preceding methods. Moreover, also the mutual relationship between the source localisation and the source separation is clarified. The source localisation methods using the source separation result and the source separation method using the source localisation result are proposed.

This paper studies a method for estimating the motion parameters [Formula: see text] using the instantaneous frequency estimates of the acoustical signal emitted from a moving target in the air and received by an acoustic sensor placed on the ground. Here, [Formula: see text] is the target’s travel speed, [Formula: see text] is the closest point of approach slant range, [Formula: see text] is the source acoustical frequency, and [Formula: see text] is the time at the target get to the closest point of approach to the sensor. A novel relationship formula between the instantaneous frequency and motion parameters is derived and an algorithm for estimating the motion parameters based on the solution of quadratic nonlinear optimization from the instantaneous frequency estimates is proposed. The instantaneous frequency is estimated by searching the peak of a polynomial Wigner–Ville distribution. An approach for calculating the optimal window width in a polynomial Wigner–Ville distribution is proposed. Numerical examples are provided for validating our theoretic claims.

Lung cancer is one of the most fatal disease with high lethality. In general lung cancers are diagnosed by radiologists. But checking radiological image is a very toilsome work for radiologists because it requires long time practice and high concentration. So, many computer-aided diagnosis (CAD) systems were introduced to cooperate with radiologists and nowadays lots of CAD systems based upon deep learning exceed human experts in diagnosing accuracy. And the remarkable thing is that the much of progress has been made in designing architectures. But, in this paper, a new pre-processing method (lung-range-standardization) is proposed in order to improve the general accuracy of lung-related diagnosis systems and to increase the utility of LIDC dataset. And the efficiency of the proposed pre-processing method is validated through comparison between the nodule segmentation model trained using lung-range-standardization and the nodule segmentation model, which is trained without lung-range-standardization.

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