Gaussian Distortion Research Articles

A Comprehensive Analysis of Various Delineation method for Exudates in Fundus Images using Miniaturized Pi Board

In current decades, employing clinical imagery analysis to automatically segregate exudates from color fundus pictures has proven to be a difficult endeavor. This paper compares the efficacy of several picture delineation techniques using a Raspberry Pi chip. By employing various techniques to regular publically accessible samples, the optimum delineation methodology is selected, while efficacy is measured using characteristics such as resemblance factors, implementation duration, sensitivity, as well as specificity. The source hue ocular pictures are initially obtained using publicly available resources. Gaussian distortion, impulse distortion, and speckle distortion could all be present in such pictures. As a result, a pre-processing approach is used to the source pictures in effort to reduce the distortion and boost brightness. After that, several delineation methods such as a thresholding technique, mean-shift algorithm, watershed algorithm, distance transform, K-means clustering, Fuzzy C-Means grouping approach and Active Contour Model are used to segment the normal and abnormal region in color fundus images. The Fuzzy C-Means grouping approach yields higher delineation precision yet requires longer execution time, according to the findings.

Highly Imbalanced Fault Diagnosis of Rolling Bearings Based on Variational Mode Gaussian Distortion and Deep Residual Shrinkage Networks

Highly Imbalanced Fault Diagnosis of Rolling Bearings Based on Variational Mode Gaussian Distortion and Deep Residual Shrinkage Networks

Ergodic capacity of correlated multiple-input–multiple-output channel with impact of transmitter impairments

Abstract The ergodic capacity of the ideal multiple-input and multiple-output (MIMO) channel grows unboundedly at the-high SNRs because of the assumption that the transceivers used in the system do not affect the signal that passes through them. In reality, the transceivers used in the MIMO system suffer from many impairments such as phase noise, quantization noise, and non-linear amplifier. These physical transceivers will affect the signal that passes through them. Thus, the influence of the physical transceivers cannot be neglected in the analysis of the MIMO channel capacity. Additionally, another essential thing that must be considered when analyzing the MIMO channel capacity is the correlation between the channel elements. Therefore, to get results close to the behavior of the practical results for the capacity of any MIMO system, this article proposes an algorithm that simulates the capacity of the correlated MIMO channel that suffers from Rayleigh flat fading with the presence of the physical transmitter. This work is done by considering that the effect of the physical transmitter at the nth transmitting antenna is a Gaussian distortion noise with a zero mean and variance proportional to the power of the signal transmitted through the same antenna. The simulation process for the proposed technique is manipulated by MATLAB software with 10,000 realizations of the channel. Also, the transmitter impairment thresholds employ in the simulation are 0.08 and 0.175. The simulation results showed that the channel capacity with the physical transmitter stopped growing at a specific limit in the very high-SNRs, and it decreased at the medium and high SNRs when the correlation coefficient increased.

Random embeddings with an almost Gaussian distortion

Let X be a symmetric, isotropic random vector in Rm and let X1...,Xn be independent copies of X. We show that under mild assumptions on ‖X‖2 (a suitable thin-shell bound) and on the tail-decay of the marginals 〈X,u〉, the random matrix A, whose columns are Xi/m exhibits a Gaussian-like behaviour in the following sense: for an arbitrary subset of T⊂Rn, the distortion supt∈T⁡|‖At‖22−‖t‖22| is almost the same as if A were a Gaussian matrix.A simple outcome of our result is that if X is a symmetric, isotropic, log-concave random vector and n≤m≤c1(α)nα for some α>1, then with high probability, the extremal singular values of A satisfy the optimal estimate: 1−c2(α)n/m≤λmin≤λmax≤1+c2(α)n/m.

Streamflow prediction with large climate indices using several hybrid multilayer perceptrons and copula Bayesian model averaging

Streamflow prediction help the modelers to manage water resources in watersheds. It gives essential information for flood control and reservoir operation. This study uses the copula-based -Bayesian model averaging (CBMA) as an improved version of the BMA model for predicting streamflow in the Golok River, the Kelantan River, the Lanas River, and the Nenggiri River of Malaysia. The CBMA corrected the assumption of the utilization of Gaussian distortion in the BMA. While the BMA used normal distribution for the variables, the CBMA uses different distribution and copula functions for the variables. This study works on the Archimedes optimization algorithm (AOA) to train the mutlilayer perceptron (MLP) model. The ability of the MLP-AOA model was benchmarked against the MLP-bat algorithm (BA), MLP-particle swarm optimization (MLP-PSO), and the MLP-firefly algorithm (MLP-FFA). The models used significant climate signals, namely, the southern oscillation index (SOI), El NiÑo–Southern Oscillation (ENSO), North Atlantic oscillation (NAO), and the pacific decadal oscillation (PDO) as the inputs to the models. The Gamma test (GT) was coupled with the AOA to provide the hybrid GT for choosing the best inputs. The gamma test was used to determine the suitable lag times of the Nino 3.4, PDO, NAO, and SOI as the inputs. The novelty of the current paper includes introducing new hybrid MLP models, new gamma test for choosing the best input combination, the comprehensive uncertainty analysis of outputs, and the use of an advanced ensemble CBMA model for predicting streamflow. First, the outputs of the MLP-AOA, MLP-BA, MLP-FFA, MLP-PSO, and MLP were obtained, following which, the CBMA as an ensemble framework based on outputs of the hybrid and standalone MLP models was used to predict monthly streamflow. The CBMA at the training level, decreased the root mean square error (RMSE) of BMA, MLP-AOA, MLP-BA, MLP-FFA, MLP-PSO, and MLP models by 28%, 32%, 52%, 53 53%, and 55%, respectively. The CBMA at the training level of another station decreased the mean absolute error (MAE) of BMA, MLP-AOA, MLP-BA, MLP-FFA, MLP-PSO, and MLP models by 6.04, 29%,42%, 49%, 52%, and 52%, respectively. The Nash Sutcliff efficiency (NSE) of the CBMA at the training level was 0.94 while it was 0.92, 0.90, 0.85, 0.84, 0.82, and 0.80 for the BMA, MLP-AOA, MLP-BA, MLP-FFA, MLP-PSO, and MLP models. The RMSE of the MLP-AOA was reported 4.3%, 12%, 14%, and 17% lower than those of the MLP-BA, MLP-FFA, MLP-PSO, and MLP models, respectively. The current research showed the CBMA and the BMA models had high abilities for predicting monthly streamflow. The results of this current study indicated that the CBMA and BMA provided lower uncertainty the standalone MLP models. The general results indicated that the streamflows in the hotter months decreased and flood control is of higher priority during the other months.

Creation of original Tamil character dataset through segregation of ancient palm leaf manuscripts in medicine

AbstractPalm leaves were one of the essential and primary sources for writing before the advent of paper. Tamil is one of the oldest southern Indian languages and among the ten oldest languages of the world. Agathiyar, a renowned Siddhar of ancient India, considered as the Father of Siddha Medicine, wrote all his therapeutic procedures only on palm leaf manuscripts, in the Tamil language. For modern day, people who try to only write and read new aspects of Tamil, identifying the ancient characters of the language is difficult. To expand readability and secure the written medicinal practices and traditions, a better recognition system is needed that can transform the ancient text images to modern ones, interpreting the ancient Tamil characters from palm leaves and understanding their contexts a time‐consuming and complicated process. Especially when it comes to medicine, the practitioners need to understand the contents of a manuscript, to apply them on a daily basis. Therefore, a recognition system is of much use to understand, interpret, and apply the techniques explained in the manuscript on a daily basis. This study is an attempt to create a considerable volume of Tamil character datasets through the segregation of ancient Tamil palm leaf manuscripts related to the field of medicine. In this study, the characters created are fed as inputs to expert systems for intelligent recognition of the context and content perceived to be present in the selected medical manuscripts. The characters have been identified in large numbers manually, and datasets are created using Gaussian distortion.

Implementation of No Reference Distortion Patch Features Image Quality Assessment Algorithm Based on Human Visual System

One of the most popular waveform based coding scheme for images is Transform coding. It is also known that the wavelet coefficients of no reference images possess certain regularities by the image distortions. The NRDPF-IQA (No Reference Distortion Patch Features-Image Quality Assessment) is one of the No-Reference Image Quality algorithms, which is the proposed algorithm in this paper. The proposed algorithm estimates quality based on features of the image. However the previous BRISQUE (Blind/Referenceless Image Spatial Quality Evaluator) algorithm operates based on low computational complexity and C-DIIVINE (complex extension of the DIIVINE) algorithm based on complex Gaussian scale mixture model. In this paper, we present NRDPF-IQA algorithm, which blindly assesses image quality based on advanced generalized Gaussian distortion model. All these distortions have characteristic features that are consistent across No-Reference images. We also compare the results with all existing algorithms. The experimental results show that this NRDPF-IQA to yield an improvement to perform the better results compare with other recent No-Reference algorithms.

Statistical Characterization and Computationally Efficient Modeling of a Class of Underwater Acoustic Communication Channels

Underwater acoustic channel models provide a tool for predicting the performance of communication systems before deployment, and are thus essential for system design. In this paper, we offer a statistical channel model which incorporates physical laws of acoustic propagation (frequency-dependent attenuation, bottom/surface reflections), as well as the effects of inevitable random local displacements. Specifically, we focus on random displacements on two scales: those that involve distances on the order of a few wavelengths, to which we refer as small-scale effects, and those that involve many wavelengths, to which we refer as large-scale effects. Small-scale effects include scattering and motion-induced Doppler shifting, and are responsible for fast variations of the instantaneous channel response, while large-scale effects describe the location uncertainty and changing environmental conditions, and affect the locally averaged received power. We model each propagation path by a large-scale gain and micromultipath components that cumulatively result in a complex Gaussian distortion. Time- and frequency-correlation properties of the path coefficients are assessed analytically, leading to a computationally efficient model for numerical channel simulation. Random motion of the surface and transmitter/receiver displacements introduce additional variation whose temporal correlation is described by Bessel-type functions. The total energy, or the gain contained in the channel, averaged over small scale, is modeled as log-normally distributed. The models are validated using real data obtained from four experiments. Specifically, experimental data are used to assess the distribution and the autocorrelation functions of the large-scale transmission loss and the short-term path gains. While the former indicates a log-normal distribution with an exponentially decaying autocorrelation, the latter indicates a conditional Ricean distribution with Bessel-type autocorrelation.

Iterational method for piecewise constant images restoration with an a priori knowledges of image objects boundaries

This article observes restoration method for piecewise constant images with predefined areas’ borders based on Van Cittert filtration. As a distortion Gaussian and rectangle impulse response are considered. Image to be reconstructed is the result of sampling and noise addition. The results of experimental research of dependency of accuracy and stop-criterion from iteration number are demonstrated. Also potential application for remote sensing image analysis such as quality enhancement of average assessments is described.

Density Functional Theory and Bose Statistics for the Freezing of Superfluid 4He

Density functional theory so far has experienced pathological results in the description of the freezing of superfluid 4He, even with the use of accurate inputs, namely the density response function, χ(q), and the equation of state of the superfluid. In fact, while the second order approximation of Ramakrishnan-Yussouff type gives a too much stable solid phase (Likos et al. in Phys. Rev. B 55:8867, 1997; Minoguchi and Galli in J. Law Temp. Phys. 162:160, 2011), a non-perturbative treatment called modified weighted density approximation (MWDA) gives, on the other hand, a too stable superfluid phase (Minoguchi et al. in J. Phys. Conf. Ser. 2012). We have preliminarily performed a MWDA calculation for a specific Gaussian distortion α=0.387 A−2 of liquid 4He. By using χ(q) and the equation of state of a system of liquid 4He in which Bose statistics has been suppressed, we have found that the calculation provides a lower energy. This indicates that the solid may be better described by a distorted normal liquid. The input data has been obtained via Path Integral Monte Carlo simulations of a system of distinguishable 4He atoms. The χ(q) has been obtained from the dynamic structure factors extracted, via the Genetic Inversion via Falsification of Theories (GIFT) method (Vitali et al. in Phys. Rev. B 82:174510, 2010) from imaginary time density–density correlation functions.

Effects of localized deformation induced by reflector antenna on received power

Based on Gaussian model for reflector antenna, the effects of localized wave-front deformation on received power are researched. It is shown that the received power depends on the deepness h, the radius a, and the distance d of the Gaussian distortion and changes regularly as they increase. Localized deformation has the greatest influence on the received power at the deepness of h ≈ 0.38 λ, which does not depend on other distortion parameters. To reduce the impact of localized deformation on the received power, the machining precision of mirrors should be much better than 0.38 λ. The maximum power penalty due to the localized distortion with different radii a is given. We hope the results can be used in the design of inter-satellite laser communication systems.

Pointing and tracking errors due to localized deformation in inter-satellite laser communication links

Instead of Zernike polynomials, ellipse Gaussian model is proposed to represent localized wave-front deformation in researching pointing and tracking errors in inter-satellite laser communication links, which can simplify the calculation. It is shown that both pointing and tracking errors depend on the center deepness h, the radiuses a and b, and the distance d of the Gaussian distortion and change regularly as they increase. The maximum peak values of pointing and tracking errors always appear around h=0.2lambda. The influence of localized deformation is up to 0.7microrad for pointing error, and 0.5microrad for tracking error. To reduce the impact of localized deformation on pointing and tracking errors, the machining precision of optical devices, which should be more greater than 0.2?, is proposed. The principle of choosing the optical devices with localized deformation is presented, and the method that adjusts the pointing direction to compensate pointing and tracking errors is given. We hope the results can be used in the design of inter-satellite lasercom systems.

Measurement of internal strain in polymers by means of optical hole burning

Spectral holes were investigated in polymers doped with octaethylporphine and other tetrapyrrolic chromophores. A single hole was burned at a fixed temperature ( T) or pressure ( P) and its width has been examined when T or P was subject to a change. The width of a hole created at higher T (30–45 K) shows surprisingly little variation upon cooling to 5 K. It even broadens slightly at the lowest T. The cooling produces negligible deviation from the Lorentzian hole shape. By contrast, the hole broadening under the He gas pressure leads to a Gaussian distortion. The reversible, non-dynamical broadening is discussed in terms of local stress affecting a small number of atoms adjacent to the dye molecule.

Melhor Estimativa da Posição de um Ponto Emissor no Espaço M-Dimensional e Determinação dos Dominios Ótimos de Busca

The best estimate of the position of an emitting point located in the m-dimensional space and conditional to the observation of the emitting point's propagation trajectories - added to gaussian distortion, is obtained. The analytical and matrix form solution developed is based on the implementation of the Maximum Likelihood Hypotesis Testing as one considers a continous m-dimensional field of hipotesis. Refering to the position of the emitting point in the m-dimensional space, gaussian conditional probability densities are derived. The optimum detection/search domains are also implemented, as junctions of the required probabilities (P) of detection or success. The results directly apply to the radio direction finding problem, electronic support measures (ESM) problem and search for submersed emitting objects.

Progressive Deformation of a Perturbed Line Vortex Filament

When a straight two-dimensional vortex filament, which is laid on the x axis, is perturbed by a three-dimensional distortion, it deforms progressively by its own induction. The progressive deformation is numerically obtained in this paper for a localized distortion y = a exp(−x2) and for a periodic distortion y = 2a cos[¼(πx)], a being the amplitude relative to the lateral extent of the distortion. When a is small, the Gaussian distortion causes a helical deformation which first moves in and then moves away toward far ends along the vortex filament, whereas the central portion where the disturbance was originally located, subsides and straightens. The plane of the sinusoidal distortion simply rotates in the direction opposite to that of the translation of fluid in the undisturbed vortex. The retrograde rotation is the same as that formulated by Kelvin. For these cases of small amplitude, a linearlized theory is also put forward. When a2 is large compared with unity, on the contrary, a nonlinear effect comes in, causing higher-order deformations to take place in both cases near the tip of the distorted pattern. This substantiates in part the author's experimental observation of the progressive deformation of a vortex loop in the final stage of boundary-layer transition. A possible mechanism of three-dimensional amplification of initially small perturbation in a shear flow is also discussed.