Related papers: Imaging Ferroelectric Domains via Charge Gradient …

We apply Principal Component Analysis (PCA) to ~100,000 stellar spectra obtained by the Sloan Digital Sky Survey (SDSS). In order to avoid strong non-linear variation of spectra with effective temperature, the sample is binned into 0.02 mag…

Cellular Automata are discrete dynamical systems that evolve following simple and local rules. Despite of its local simplicity, knowledge discovery in CA is a NP problem. This is the main motivation for using data mining techniques for CA…

Microscopic imaging of local magnetic fields provides a window into the organizing principles of complex and technologically relevant condensed matter materials. However, a wide variety of intriguing strongly correlated and topologically…

Principal Component Analysis (PCA)-based techniques can separate data into different uncorrelated components and facilitate the statistical analysis as a pre-processing step. Independent Component Analysis (ICA) can separate statistically…

Ferroelectrics form domain patterns that minimize their energy subject to imposed boundary conditions. In a linear, constrained theory, that neglects domain wall energy, periodic domain patterns in the form of multi-rank laminates can be…

For over 70 years, ferroelectric materials have been remaining one of the central research topics for condensed matter physics and material science, the interest driven both by fundamental science and applications. However, ferroelectric…

A stochastical description is applied in order to understand how ferroelectric structures can be formed. The predictions are compared with experimental data of the so-called electrical fixing: Domains are patterned in photorefractive…

We present a method of measuring the charge density on dielectric surfaces. Similar to electrostatic force microscopy we record the electrostatic interaction between the probe and the sample surface, but at large tip-sample distances. For…

Regularized variants of Principal Components Analysis, especially Sparse PCA and Functional PCA, are among the most useful tools for the analysis of complex high-dimensional data. Many examples of massive data, have both sparse and…

Piezoresponse Force Microscopy (PFM), as a powerful nanoscale characterization technique, has been extensively utilized to elucidate diverse underlying physics of ferroelectricity. However, the intensive study of conventional PFM has…

In many atmospheric and earth sciences, it is of interest to identify dominant spatial patterns of variation based on data observed at $p$ locations and $n$ time points with the possibility that $p>n$. While principal component analysis…

We present here the design of a sensitive Compact Faraday-modulator (CFM) based optical magnetometer for imaging the distribution of weak local magnetic fields inside hysteretic magnetic materials. The system developed has a root mean…

Ferroelectric domain wall conductivity (DWC) is an intriguing functional property, that can be controlled through external stimuli such as electric and mechanical fields. Optical-field control, as a non-invasive flexible handle, has rarely…

Magnetic Particle Imaging (MPI) is a promising imaging modality that tracks magnetic nanoparticles (MNPs) to generate real time, high-resolution images. However, achieving an optimal balance between strong signal strength and sharp image…

Pb-based perovskites with multiple cations are fascinating materials showing various phenomena such as high piezoelectric, electromechanical, and relaxor properties. While chemical disordering accompanied by polar nanoregions and nanosized…

Using direct variational method with 2-parametric trial function and Landau-Ginzburg-Devonshire thermodynamical approach, we derived analytical expressions for polarization spatial redistribution in the ferroelectrics caused by the biased…

Domain walls in ferroelectrics exhibit a plethora of phases and functionalities not found in the bulk. The interplay of electrostatic, chemical, topological, and distortive inhomogeneities at the walls can be so complex, however, that this…

Nanoscale ferroelectrics that can be integrated into microelectronic fabrication processes are highly desirable for low-power computing and non-volatile memory devices. However, scalable novel ferroelectric materials, such as hafnium oxide…

Ferroelectric materials display exotic polarization textures at the nanoscale that could be used to improve the energetic efficiency of electronic components. The vast majority of studies were conducted in two dimensions on thin films, that…

Principal component analysis (PCA) is a dimensionality reduction method in data analysis that involves diagonalizing the covariance matrix of the dataset. Recently, quantum algorithms have been formulated for PCA based on diagonalizing a…