Abstract: We proposed a concept of balanced switching converter circuit, which is an effective way to reduce the common-mode conducted noise by current cancellation. In this paper, the path of the noise current in a balanced boost converter circuit is investigated using a current probe. By measuring current waveforms and frequency spectra of the parasitic capacitances and the frame ground line,...

In this paper the neural network based lter for nonlinear interference cancellation is developed. The Hyper Radial Basis Function (HRBF) network with associated Manhattan learning algorithm is proposed for non-linear noise cancellation under assumption that reference noise is available. The HRBF network is a generalization of radial basis function (RBF) and generalized radial basis function (GR...

Microphone arrays are powerful tools for noise suppression in a reverberant room. Generalized Sidelobe Canceller (GSC) that exploits Minimum Variance (MV) criterion is efficient in interference suppression when there is no correlation between the desired signal and the interferences. Correlation between the desired signal and any of interference produces a desired signal cancellation and degrad...

A noise-canceling processor is developed for matched-field processing problems involving a signal buried in noise. This processor is based on modeling both signal and noise and searching the space of unknown parameters to achieve the best agreement between covariances. The noise-canceling processor educes to the Bartlett processor in the limit of high signal-to-noise ratio. The examples illustr...

In this paper, the MIMO full-duplex system is proposed to remove self-interference signal using modified DFT based channel estimation technique. In SISO full-duplex systems only single transceiver can transmit data on either side simultaneously whereas in MIMO full-duplex systems multiple transceivers can transmit data simultaneously from both sides. During MIMO full-duplex transmission self-in...

In this paper an adaptive approach to cancellation of additive, convolutional noise from many{source mixtures with a simultaneous blind source separation is proposed. Associated neural network learning algorithms are developed on the basis of decorrelation principle and energy minimization of output signals. The reference noise is transformed into a convolutional one by employing an adaptive FI...