Several methods of combining a number of time series into a single series are discussed. They are all individual filtering followed by summation and are somewhat like Wiener filtering in that a least squares criterion is used to define the filter coefficients. They differ from Wiener filtering in that signal information is given in the form of various linear constraints on the filter coefficien...

In this paper, we propose a novel approach for single channel speech enhancement by exploiting the correlation among 2D transform coefficients, which has been previously neglected by traditional speech enhancement methods. Our approach makes use of a time-frequency representation (spectrogram) of the input signal and a novel 2D spectrogram filter (2DSF)is designed to provide a good estimate of ...

this paper studies the use of unscented kalman filters (ukf) to estimate nonlinear dynamics and, specifically, adaptive determination of scaling parameters in these filters. due to lack of analytic solution and use of numerical methods instead, the computational load of these filters increases drastically. in this paper, a new method is proposed based on interactive multiple models (imm) which ...

The particle filter (PF) is a novel technique that has sufficiently good estimation results for the nonlinear/non-Gaussian systems. However, PF is inconsistent that caused mainly by loss of particle diversity in resampling step and unknown a priori knowledge of the noise statistics. This paper introduces a new modified particle filter called adaptive unscented particle filter (AUPF) to overcome th...

In this paper we study the Poisson and heat equations on bounded and unbounded domains with smooth boundary with random Dirichlet boundary conditions. The main novelty of this work is a convenient framework for the analysis of such equations excited by the white in time and/or space noise on the boundary. Our approach allows us to show the existence and uniqueness of weak solutions in the space...

Nowadays Medical imaging technique Magnetic Resonance Imaging (MRI) plays an important role in medical setting to form high standard images contained in the human brain. MRI is commonly used once treating brain, prostate cancers, ankle and foot. The Magnetic Resonance Imaging (MRI) images are usually liable to suffer from noises such as Gaussian noise, salt and pepper noise and speckle noise. S...

Echocardiographic images are inherent with speckle noise which makes visual reading and analysis quite difficult. The multiplicative speckle noise masks finer details, necessary for diagnosis of abnormalities. A novel speckle reduction technique based on integration of geometric, wiener, and fuzzy filters is proposed and analyzed in this paper. The denoising applications of fuzzy filters are st...

In this paper, we propose a Correlation-Maximization denoising filter which utilizes periodicity information to remove additive noise in bird calls. We also developed a statistically-based noise robust bird-call classification system which uses the denoising filter as a frontend. Enhanced bird calls which are the output of the denoising filter are used for feature extraction. Gaussian Mixture M...

The knowledge of Modulation Transfer Function (MTF) for a degraded image provides a mean to compensate for the image degradation, which improves the image quality in terms of sharpness. MTF compensating is in the image restoration; hence it is fundamentally an ill-posed problem. This paper proposes a stable and flexible filtering technique that executes an optimal tradeoff between sharpness and...

In this paper, we present a new single-channel noise reduction method that integrates compensation and soft masking into the same statistical model assumptions for noise-robust speech recognition. By utilizing a Gaussian mixture model(GMM) as a pre-knowledge of speech and added noise signals, the proposed method can effectively restore clean speech spectra and separate out ambient noises from a...