Intersection Of Confidence Intervals Rule Research Articles

Gravitational-Wave Burst Signals Denoising Based on the Adaptive Modification of the Intersection of Confidence Intervals Rule.

Gravitational-wave data (discovered first in 2015 by the Advanced LIGO interferometers and awarded by the Nobel Prize in 2017) are characterized by non-Gaussian and non-stationary noise. The ever-increasing amount of acquired data requires the development of efficient denoising algorithms that will enable the detection of gravitational-wave events embedded in low signal-to-noise-ratio (SNR) environments. In this paper, an algorithm based on the local polynomial approximation (LPA) combined with the relative intersection of confidence intervals (RICI) rule for the filter support selection is proposed to denoise the gravitational-wave burst signals from core collapse supernovae. The LPA-RICI denoising method’s performance is tested on three different burst signals, numerically generated and injected into the real-life noise data collected by the Advanced LIGO detector. The analysis of the experimental results obtained by several case studies (conducted at different signal source distances corresponding to the different SNR values) indicates that the LPA-RICI method efficiently removes the noise and simultaneously preserves the morphology of the gravitational-wave burst signals. The technique offers reliable denoising performance even at the very low SNR values. Moreover, the analysis shows that the LPA-RICI method outperforms the approach combining LPA and the original intersection of confidence intervals (ICI) rule, total-variation (TV) based method, the method based on the neighboring thresholding in the short-time Fourier transform (STFT) domain, and three wavelet-based denoising techniques by increasing the improvement in the SNR by up to 118.94% and the peak SNR by up to 138.52%, as well as by reducing the root mean squared error by up to 64.59%, the mean absolute error by up to 55.60%, and the maximum absolute error by up to 84.79%.

Sparse time–frequency distributions based on the $$\ell _1$$ ℓ 1 -norm minimization with the fast intersection of confidence intervals rule

Methods based on the sparsity constraint have been recently introduced to the time–frequency (TF) signal processing, achieving artifact suppression by exploiting the fact that most real-life signals are sparse in the TF domain. In this paper, we propose a sparse reconstruction algorithm based on the two-step iterative shrinkage/thresholding (TwIST) algorithm. In the proposed TwIST algorithm modification, the soft-thresholding value is adaptively determined by the fast intersection of the confidence intervals (FICI) rule in each iteration of the reconstruction algorithm. The FICI rule is used to determine the TF region with the lowest mean value, and the soft-thresholding value is set to the largest sample value inside the region. The performance of the proposed algorithm has been compared to the performance of the state-of-the-art reconstruction algorithms in terms of their execution time and concentration of the resulting TF distribution.

Relative intersection of confidence intervals rule for sharper restoration of soft x-ray images.

We present a novel method for restoration of images of nanostructures obtained with a soft-ray microscope that uses a 46.9 nm soft x-ray laser microscope for illumination. To suppress the noise and to preserve the image sharpness, we develop a method based on pixel adaptive zero-order modeling of the observed object. Neighboring areas of each pixel are selected using the relative intersection of confidence intervals rule and used for restoration. Due to the non-uniform distribution of noise in the images, we use robust spatial noise modeling. The method provides sharp restored images-sharper than competitive approaches. The sharpness is measured using local phase coherence in the complex wavelet transform domain and shows visible improvement of the novel method.

Multidimensional smoothing by adaptive local kernel-weighted log-likelihood: Application to long-term care insurance

We are interested in modeling the mortality of long-term care (LTC) claimants having the same level of severeness (heavy claimant). Practitioners often use empirical methods that rely heavily on expert opinions. We propose approaches not depending on an expert’s advice. We analyze the mortality as a function of both the age of occurrence of the claim and the duration of the care. LTC claimants are marked by a relatively complex mortality pattern. Hence, rather than using parametric approaches or models with expert opinions, adaptive local likelihood methods allow us to extract the information from the data more pertinently. We characterize a locally adaptive smoothing pointwise method using the intersection of confidence intervals rule, as well as a global method using local bandwidth correction factors. The latter is an extension of the adaptive kernel method proposed by Gavin et al. (1995) to likelihood techniques. We vary the amount of smoothing in a location-dependent manner and allow adjustments based on the reliability of the data. Tests, and single indices summarizing the lifetime probability distribution are used to compare the graduated series obtained by adaptive local kernel-weighted log-likelihoods to p-spline and local likelihood models.

A just-in-time adaptive classification system based on the intersection of confidence intervals rule

Classification systems meant to operate in nonstationary environments are requested to adapt when the process generating the observed data changes. A straightforward form of adaptation implementing the instance selection approach suggests releasing the obsolete data onto which the classifier is configured by replacing it with novel samples before retraining. In this direction, we propose an adaptive classifier based on the intersection of confidence intervals rule for detecting a possible change in the process generating the data as well as identifying the new data to be used to configure the classifier. A key point of the research is that no assumptions are made about the distribution of the process generating the data. Experimental results show that the proposed adaptive classification system is particularly effective in situations where the process is subject to abrupt changes.

Separable Image Denoising Based on the Relative Intersection of Confidence Intervals Rule

In this paper we have proposed a novel method for image denoising using local polynomial approximation (LPA) combined with the relative intersection of confidence intervals (RICI) rule. The algorithm performs separable column-wise and row-wise image denoising (i.e., independently by rows and by columns), combining the obtained results into the final image estimate. The newly developed method performs competitively among recently published state-of-the-art denoising methods in terms of the peak signal-to-noise ratio (PSNR), even outperforming them for small to medium noise variances for images that are piecewise constant along their rows and columns.

Estimation of the phase derivative using an adaptive window spectrogram

The paper introduces an adaptive-window-spectrogram-based method to directly estimate the phase derivative from a single fringe pattern. The proposed method relies on estimating the phase derivative using spectrogram peak detection for a set of different window lengths. Then the optimal window length is selected from the set by resolving the estimator's bias variance trade-off using the intersection of confidence intervals rule. Finally, the phase derivative estimate corresponding to the optimum window is selected. The method's applicability to phase derivative estimation is demonstrated using simulation and experimental results.

Adaptive window Wigner-Ville-distribution-based method to estimate phase derivative from optical fringes

We introduce an adaptive window Wigner-Ville-distribution-based method to directly estimate the phase derivative from a single fringe pattern. In the proposed method, the phase derivative is estimated by using the peak detection of the pseudo-Wigner-Ville distribution for a set of different window lengths. Then the optimal window length is selected from the set by resolving the estimator's bias variance trade-off, using the intersection of confidence intervals rule. Finally, the phase derivative estimate corresponding to the optimum window is selected. Simulation and experimental results are presented to demonstrate the method's applicability for the phase derivative estimation.

Edge-preserving adaptive wavelet denoising using ICI rule

An adaptive lifting scheme is proposed, in which the intersection of confidence intervals (ICI) rule is used to determine the wavelet support. The adaptation is performed on a point-by-point basis and the ICI rule is used to prevent the support from spanning across the edges in a signal, in which the edge is considered to be any sudden change in the signal statistics. It is shown that the proposed method preserves the edges well, and the overall denoising performance is much better than in the other considered methods.

A Signal Denoising Method Based on the Improved ICI Rule

In this letter, we have proposed a signal denoising method based on a modification of the intersection of confidence intervals (ICI) rule. The ICI rule is complemented by the relative intersection of confidence intervals length which is used as an additional criterion for adaptive filter support selection. It is shown that the proposed method outperforms the original ICI method equipped with the local polynomial approximation (LPA), as well as various conventional wavelet shrinkage methods.

Mean shift algorithm equipped with the intersection of confidence intervals rule for image segmentation

The mean shift algorithm equipped with the intersection of confidence intervals (ICI) rule for image smoothing and segmentation is proposed. Firstly, the ICI rule for bandwidth selection in a multi-dimensional feature space is studied. In the ICI rule, the kernel function is used to estimate the probability density intervals of the pixel feature and find its bandwidth close to the optimal parameter. Secondly, the mean shift algorithm is used for image smoothing and segmentation with the bandwidth determined by the ICI rule. Experimental results show that the structures of the objects in images are preserved and over-segmentation caused by noises and texture can be eliminated effectively. In addition, a comparison between the smoothing results with adaptive bandwidths determined by the ICI rule and with fixed bandwidths is done. The results show that the proposed method is better in the field of image smoothing and segmentation.

Adaptive Window Size Image De-noising Based on Intersection of Confidence Intervals (ICI) Rule

We describe a novel approach to solve a problem of window size (bandwidth) selection for filtering an image signal given with a noise. The approach is based on the intersection of confidence intervals (ICI) rule and gives the algorithm, which is simple to implement and nearly optimal in the point-wise mean squared error risk. The local polynomial approximation (LPA) is used in order to derive the 2D transforms (filters) and demonstrate the efficiency of the approach. The ICI rule gives the adaptive varying window size and enables the algorithm to be spatially adaptive in the sense that its quality is close to that which one could achieve if the smoothness of the estimated signal was known in advance. Optimization of the threshold (design parameter of the ICI) is studied. It is shown that the cross-validation adjustment of the threshold significantly improves the algorithm accuracy. In particular, simulation demonstrates that the adaptive transforms with the adjusted threshold parameter perform better than the adaptive wavelet estimators.

Adaptive Varying Window Size Filtering of Color Images Based on Intersection of Confidence Intervals Rule

Adaptive Varying Window Size Filtering of Color Images Based on Intersection of Confidence Intervals Rule