Enhanced 2D plotting method for scanning probe microscopy imaging

An enhanced 2D plotting method for scanning probe microscopy imaging implementing a gradient-based value mapping for pseudocolor images and its application to studies of epitaxial layer surface morphology is presented. It is demonstrated that this method is capable of revealing the finest features on growth surfaces. Presence of elementary growth steps on the surface of flat-topped hillocks found on Hg₀.₈Cd₀.₂Te LPE-grown epitaxial layers, examples of cooperative effects of screw dislocations on PbTe and Hg₁₋xCdxTe epilayer growth as well as atypical surface morphology of PbTe epilayers are discussed

The color excitable Schrodinger metamedium

In this work, we apply quantum cellular automata (QCA) to study pattern formation and image processing in quantum-diffusion Schrodinger systems (QDSS) with triplet-valued (color-valued) diffusion coefficients. Triplet numbers have the real part and two imaginary parts (with two imaginary units).They form 3-D triplet algebra. Discretization of the Schrodinger equation gives ”lattice based metamaterial models” with various triplet–valued physical parameters. The process of excitation in these media is described by the Schrodinger equations with the wave functions that have values in triplet algebras. If a traditional computer is thought of as a ”programmable object”,QDSS in the form of QCA is a computer of new kind and is better visualized as a ”programmable material”. The purpose of this work is to introduce new metamedium in the form of cellular automata. The cells are placed in a 2-D array and they are capable of performing basic arithmetic operating in the triplet algebra and exchanging massages about their state. Cellular automata like architectures have been successfully used for computer vision problems and color image processing. Such metamedia possess large opportunities in processing of color images in comparison with the ordinary diffusion media with the real-valued diffusion coefficients. The latter media are used for creation of the eye-prosthesis(so called the ”silicon eye”). The color metamedium suggested can serve as the prosthesis prototype for perception of the color images.В данной работе мы используем квантовый клеточный автомат (ККА) для изучения базовых закономерностей и процесса обработки изображений в системах с квантовой диффузией Шредингера с триплетнозначными (цветными) коэффициентами диффузии. Триплетные числа имеют действительную часть и две мнимых компоненты (2 мнимые единицы). Они формируют трехмерную алгебру. Дискретизация уравнения Шредингера дает "решатчатые" метаматериальные модели с различными триплетнозначными физическими параметрами. Процесс распространения возбуждения в таких средах описывается уравнениями Шредингера с волновыми функциями, которые принимают значения в триплетных алгебрах. Если об обыкновенном компьютере можно говорить как о "программируемом объекте", система с квантовой диффузией Шредингера в форме ККА - это компьютер нового типа, который лучше иллюстрируется понятием "программируемый материал". Цель данной работы - представить новую метасреду в форме клеточного автомата. Клетки размещены в 2D массиве, они могут выполнять базовые арифметические операции в триплетной алгебре и обмениваться сообщениями об их состояниях. Клеточный автомат как архитектурная модель успешно используется для решения задач компьютерного зрения и обработки цветных изображений. Новая среда обладает широкими возможностями по обработке цветных изображений по сравнению со средой с обыкновенной диффузией (коэффициент диффузии - действительное число). Данные метасреды используются в медицине для создания так называемого "кремниевого глаза". Предложенная среда может служить прототипом такого искусственного глаза для восприятия цветных изображений

OmicsVis: an interactive tool for visually analyzing metabolomics data

When analyzing metabolomics data, cancer care researchers are searching for differences between known healthy samples and unhealthy samples. By analyzing and understanding these differences, researchers hope to identify cancer biomarkers. Due to the size and complexity of the data produced, however, analysis can still be very slow and time consuming. This is further complicated by the fact that datasets obtained will exhibit incidental differences in intensity and retention time, not related to actual chemical differences in the samples being evaluated. Additionally, automated tools to correct these errors do not always produce reliable results. This work presents a new analytics system that enables interactive comparative visualization and analytics of metabolomics data obtained by two-dimensional gas chromatography-mass spectrometry (GC × GC-MS). The key features of this system are the ability to produce visualizations of multiple GC × GC-MS data sets, and to explore those data sets interactively, allowing a user to discover differences and features in real time. The system provides statistical support in the form of difference, standard deviation, and kernel density estimation calculations to aid users in identifying meaningful differences between samples. These are combined with novel transfer functions and multiform, linked visualizations in order to provide researchers with a powerful new tool for GC × GC-MS exploration and bio-marker discovery

Gradient-based value mapping for pseudocolor images

We develop a method for automatic colorization of images (or two-dimensional fields) in order to visualize pixel values and their local differences. In many applications, local differences in pixel values are as important as their values. For example, in topography, both elevation and slope often must be considered. Gradient based value mapping (GBVM) is a technique for colorizing pixels based on value (e.g., intensity or elevation) and gradient (e.g., local differences or slope). The method maps pixel values to a color scale (either gray-scale or pseudocolor) in a manner that emphasizes gradients in the image while maintaining ordinal relationships of values. GBVM is especially useful for high-precision data, in which the number of possible values is large. Colorization with GBVM is demonstrated with data from comprehensive two-dimensional gas chromatography (GCxGC), using both gray-scale and pseudocolor to visualize both small and large peaks, and with data from the Global Land One-Kilometer Base Elevation (GLOBE) Project, using gray scale to visualize features that are not visible in images produced with popular value-mapping algorithms

Gradient-based value mapping for pseudocolor images

We develop a method for automatic colorization of images (or two-dimensional fields) in order to visualize pixel values and their local differences. In many applications, local differences in pixel values are as important as their values. For example, in topography, both elevation, and slope often must be considered. Gradient-based value mapping (GBVM) is a technique for colorizing pixels based on value (e.g., intensity or elevation) and gradient (e.g., local differences or slope). The method maps pixel values to a color scale (either gray-scale or pseudocolor) in a manner that emphasizes gradients in the image while maintaining ordinal relationships of values. GBVM is especially useful for high-precision data, in which the number of possible values is large. Colorization with GBVM is demonstrated with data from comprehensive two-dimensional gas chromatography (GCxGC), using both gray-scale and pseudocolor to visualize both small and large peaks, and with data from the Global Land One-Kilometer Base Elevation (GLOBE) Project, using grayscale to visualize features that are not visible in images produced with popular value-mapping algorithms