Cone Monotonicity: Structure Theorem, Properties, and Comparisons to Other Notions of Monotonicity

In search of a meaningful 2-dimensional analog to mono- tonicity, we introduce two new definitions and give examples of and dis- cuss the relationship between these definitions and others that we found in the literature. Note: After we published the article in Abstract and Applied Analysis and after we searched multiple times for previous work, we discovered that Clarke at al. had introduced the definition of cone monotonicity and given a characterization. See the addendum at the end of this paper for full reference information

Nonasymptotic Densities for Shape Reconstruction

In this work, we study the problem of reconstructing shapes from simple nonasymptotic densities measured only along shape boundaries. The particular density we study is also known as the integral area invariant and corresponds to the area of a disk centered on the boundary that is also inside the shape. It is easy to show uniqueness when these densities are known for all radii in a neighborhood ofr=0, but much less straightforward when we assume that we only know the area invariant and its derivatives for only oner>0. We present variations of uniqueness results for reconstruction (modulo translation and rotation) of polygons and (a dense set of) smooth curves under certain regularity conditions

The Finite Exact Algorithm for Image Analysis

New data techniques are being sought for real-time video and image analysis. Recently, Moon created a fast, finite algorithm, the hyperplane traversal (ht) algorithm, for de-noising 1D signals. Here we will present our extension of the existing algorithm to 2D images

Phase State Curves: An Exploration of Clausius Clapeyron and Antoine Equations

In an industrial setting having a simple yet precise equation to calculate the conditions for the liquid to gas phase change is important to optimize the energy used in production. The current approximations, obtained from experimental data, seem to use functions that are more complicated than the curve suggests are necessary. In this project, a best approximating curve will be presented using techniques from optimization, chemistry, and data analytics. Best is defined to mean that the solution can be described according to the science of CC equation

Oscillatory Dynamics of Single Bubbles and Agglomeration in a Sound Field in Microgravity

A dual-frequency acoustic levitator containing water was developed for studying bubble and drop dynamics in low gravity. It was flown on USML-1 where it was used in the Glovebox facility. High frequency (21 or 63 kHz) ultrasonic waves were modulated by low frequencies to excite shape oscillations on bubbles and oil drops ultrasonically trapped in the water. Bubble diameters were typically close to 1 cm or larger. When such large bubbles are acoustically trapped on the Earth, the acoustic radiation pressure needed to overcome buoyancy tends to shift the natural frequency for quadrupole (n = 2) oscillations above the prediction of Lamb's equation. In low gravity, a much weaker trapping force was used and measurements of n = 2 and 3 mode frequencies were closer to the ideal case. Other video observations in low gravity include: (i) the transient reappearance of a bulge where a small bubble has coalesced with a large one, (ii) observations of the dynamics of bubbles coated by oil indicating that shape oscillations can shift a coated bubble away from the oil-water interface of the coating giving a centering of the core, and (iii) the agglomeration of bubbles induced by the sound field

Characteristic Shape Sequences for Measures on Images

Researchers in many fields often need to quantify the similarity between images using metrics that measure qualities of interest in a robust quantitative manner. We present here the concept of image dimension reduction through characteristic shape sequences. We formulate the problem as a nonlinear optimization program and demonstrate the solution on a test problem of extracting maximal area ellipses from two-dimensional image data. To solve the problem numerically, we augment the class of mesh adaptive direct search (MADS) algorithms with a filter, so as to allow infeasible starting points and to achieve better local solutions. Results here show that the MADS filter algorithm is successful in the test problem of finding good characteristic ellipse solutions from simple but noisy images

An Efficient Class of Direct Search Surrogate Methods for Solving Expensive Optimization Problems with CPU-Time-Related Functions

In this paper, we characterize a new class of computationally expensive optimization problems and introduce an approach for solving them. In this class of problems, objective function values may be directly related to the computational time required to obtain them, so that, as the optimal solution is approached, the computational time required to evaluate the objective is significantly less than at points farther away from the solution. This is motivated by an application in which each objective function evaluation requires both a numerical fluid dynamics simulation and an image registration process, and the goal is to find the parameter values of a predetermined reference image by comparing the flow dynamics from the numerical simulation and the reference image through the image comparison process. In designing an approach to numerically solve the more general class of problems in an efficient way, we make use of surrogates based on CPU times of previously evaluated points, rather than their function values, all within the search step framework of mesh adaptive direct search algorithms. Because of the expected positive correlation between function values and their CPU times, a time cutoff parameter is added to the objective function evaluation to allow its termination during the comparison process if the computational time exceeds a specified threshold. The approach was tested using the NOMADm and DACE MATLAB software packages, and results are presented