We propose a method to obtain a fringe-free extended focused image in white light scanning interference microscopy based on processing the stack of images over a range within the coherence length of the source.
If the appropriate phase and/or amplitude profile is placed on a Diffractive Optical Element (DOE) it can practically generate an image of an object (hologram) by diffraction of the light. The problem of generating computer holograms consists of calculating...
Tsamparlis et al. [3] have developed a systematic method for computing of the conformal algebra of 1+3 space-times. The proper CKV’s are
This paper introduces a methodology aimed at validating anomalies identified through unsupervised techniques. Our approach is grounded in the assumption that machine learning models perform optimally when trained on data free of anomalies. Therefore, to assess the veracity of anomalies pinpointed by an unsupervised method, we undertake a two-fold process. Initially, anomalies are removed from the training dataset. Subsequently, we gauge the expected enhancement in the performance of classification models. To evaluate the effectiveness of our methodology, we employed three well-established unsupervised anomaly detection techniques: Local Outlier Factor (LOF), Isolation Forest (iForest), and Autoencoders. These methods were complemented by a voting system designed to identify anomalous data records. The reliability of these detected anomalies was rigorously tested using various classification models, including K-Nearest Neighbors, Logistic Regression, Decision Tree, Random Forest, AdaBoost, and Support Vector Classifier (SVC). This evaluation was conducted both before and after the removal of anomalies from the training dataset. Our methodology underwent rigorous testing across five distinct datasets: Breast Cancer, German Credit, Diabetes, Heart Failure Disease, and Titanic Survivor. The results provide evidence of its effectiveness, with notable improvements observed in the classification performance across four of the five datasets.
The main objective of this research is to analyze the effect of applying some image preprocessing techniques on the performance of convolutional neural networks for the classification of COVID19, Pneumonia, and Normal patients from chest X-ray images. The normal...
We propose a method for calculating a bandlimited diffuser with smooth spectrum in the Fresnel domain without any 2π phase ambiguities using the fractional Fourier transform. Such diffusers are necessary to avoid problems of speckles.
An exact solution of the Einstein-Maxwell theory is presented. The solution represents the metric of a radiating charged particle embedded in a de Sitter universe.
This paper proposes an approach to facilitate the process of individualization of patients from their medical images, without compromising the inherent confidentiality of medical data. The identification of a patient from a medical image is not often the goal of security methods applied to image records. Usually, any identification data is removed from shared records, and security features are applied to determine ownership. We propose a method for embedding a QR-code containing information that can be used to individualize a patient. This is done so that the image to be shared does not differ significantly from the original image. The QR-code is distributed in the image by changing several pixels according to a threshold value based on the average value of adjacent pixels surrounding the point of interest. The results show that the code can be embedded and later fully recovered with minimal changes in the UIQI index - less than 0.1% of different.
Recently obtained results linking several constants of motion to one (non-Noetherian) symmetry to the problem of geodesic motion in Riemannian space-times are applied. The construction of conserved quantities in geodesic motion as well as the deduction of geometrical statements about Riemannian space-times are achieved.
No abstract is provided for this article.
Electroencephalography (EEG) is a non-invasive and cost-effective method for studying the complexity of brain dynamics. In this study, we constructed Minimum Sp
