We present a novel nonparametric adaptive partitioning and stitching (NAPS) algorithm to estimate probability density function (PDF) of single variable. Sampled data is partitioned into blocks using branching tree that minimizes deviations from uniform within various sample sizes arranged in staggered format. The block are constructed balance the load parallel computing as PDF for each independ...

Most independent component analysis (ICA) algorithms use mutual information (MI) measures based on Shannon entropy as a cost function, but Shannon entropy is not the only measure in the literature. In this paper, instead of Shannon entropy, Tsallis entropy is used and a novel ICA algorithm, which uses kernel density estimation (KDE) for estimation of source distributions, is proposed. KDE is di...

The major issue associated with ultrasound imaging is that it may be corrupted by speckle noise during image acquicition. Speckle pattern is a form of multiplicative noise which blurs the ultrasound images. It reduces the contrast and resolution of ultrasound images which results in poor interpretation of image features. Hence speckle reduction is often used as a preprocessing step for successf...

In this paper we introduce an efficient method to unwrap multi-frequency phase estimates for time-of-flight ranging. The algorithm generates multiple depth hypotheses and uses a spatial kernel density estimate (KDE) to rank them. The confidence produced by the KDE is also an effective means to detect outliers. We also introduce a new closed-form expression for phase noise prediction, that bette...

In many algorithms for background modeling, a distribution over feature values is modeled at each pixel. These models, however, do not account for the dependencies that may exist among nearby pixels. The joint domain-range kernel density estimate (KDE) model by Sheikh and Shah [7], which is not a pixel-wise model, represents the background and foreground processes by combining the three color d...

BACKGROUND Geographic information systems have advanced the ability to both visualize and analyze point data. While point-based maps can be aggregated to differing areal units and examined at varying resolutions, two problems arise 1) the modifiable areal unit problem and 2) any corresponding data must be available both at the scale of analysis and in the same geographic units. Kernel density e...

The nature of the kernel density estimator (KDE) is to find underlying probability function (p.d.f) for a given dataset. key training KDE determine optimal bandwidth or Parzen window. All data points share fixed (scalar univariate and vector multivariate KDE) in (FKDE). In this paper, we propose an improved variable (IVKDE) which determines each point dataset based on integrated squared error (...

Monte Carlo radiation transport simulations have clearly contributed to improve the design of nuclear systems. When performing in-beam or shielding a complexity arises due fact that particles must be tracked regions far from original source behind shielding, often lacking sufficient statistics. Different possibilities overcome this problem such as using particle lists generating synthetic sourc...