Simulation of multiple etch fronts

We developed a series of algorithms, based on the string model, that simulate the inhomogeneous etching process of polymeric materials, which exhibit swelling during dissolution by an organic solvent, used in IC process fabrication. The swelling creates a gel layer between the solid (polymer) and the liquid (solvent) phases. Usually, simulations based on the string development model assume an abrupt solid-liquid interface. Our algorithms are capable of simulating two interrelated etch fronts, and may easily be extended to simulate n interrelated etch fronts. The speed with which the points of the gel-solid boundary advance depends on the distance of each point from the gel-liquid boundary. Considering the two boundaries as plane curves, one must deal with the problem of what we define as the “distance” of every point of the first curve from the second. The algorithm developed can deal with any pair of non-intersecting curves in two dimensions. Two pairs of curves were used to test the algorithm: the first with curves of a simple form, and the second with curves of a complicated form. Next we simulated the development process of an e-beam resist that exhibits swelling. The results were perfect for the minimum distance subalgorithm, and they predicted a longer development time for the e-beam resist compared with that required for a non-swelling resist.     

Graph-based multimodal clustering for social multimedia

Real world datasets often consist of data expressed through multiple modalities. Clustering such datasets is in most cases a challenging task as the involved modalities are often heterogeneous. In this paper we propose a graph-based multimodal clustering approach. The proposed approach utilizes an example relevant clustering in order to learn a model of the “same cluster” relationship between a pair of items. This model is subsequently used in order to organize the items of the collection to be clustered in a graph, where the nodes represent the items and a link between a pair of nodes exists if the model predicted that the corresponding pair of items belong to the same cluster. Eventually, a graph clustering algorithm is applied on the graph in order to produce the final clustering. The proposed approach is applied on two problems that are typically treated using clustering techniques; in particular, it is applied on the problem of detecting social events and to the problem of discovering different landmark views in collections of social multimedia.     

Effects of Errors in the Viewing Geometry on Shape Estimation

A sequence of images acquired by a moving sensor contains information about the three-dimensional motion of the sensor and the shape of the imaged scene. Interesting research during the past few years has attempted to characterize the errors that arise in computing 3D motion (egomotion estimation) as well as the errors that result in the estimation of the scene's structure (structure from motion). Previous research is characterized by the use of optic flow or correspondence of features in the analysis as well as by the employment of particular algorithms and models of the scene in recovering expressions for the resulting errors. This paper presents a geometric framework that characterizes the relationship between 3D motion and shape in the presence of errors. We examine how the three-dimensional space recovered by a moving monocular observer, whose 3D motion is estimated with some error, is distorted. We characterize the space of distortions by its level sets, that is, we characterize the systematic distortion via a family of iso-distortion surfaces, which describes the locus over which the depths of points in the scene in view are distorted by the same multiplicative factor. The framework introduced in this way has a number of applications: Since the visible surfaces have positive depth (visibility constraint), by analyzing the geometry of the regions where the distortion factor is negative, that is, where the visibility constraint is violated, we make explicit situations which are likely to give rise to ambiguities in motion estimation, independent of the algorithm used. We provide a uniqueness analysis for 3D motion analysis from normal flow. We study the constraints on egomotion, object motion, and depth for an independently moving object to be detectable by a moving observer, and we offer a quantitative account of the precision needed in an inertial sensor for accurate estimation of 3D motion.     

Blast response simulation of an elastic structure: Evaluation of the fluid–structure interaction effect

Blast pressure wave interaction with an elastic structure is investigated using a numerical analysis approach, which considers fluid–structure interaction (FSI) within an Arbitrary Lagrange Euler (ALE) framework. Approximate numerical procedures for solving the Riemann problem associated with the shock are implemented within the Godunov finite volume scheme for the fluid domain. The structural displacement predicted by ignoring FSI is larger than the corresponding displacement considering FSI. The influence of the structural and blast pressure wave parameters on the importance of FSI is studied using an analysis of variables. Two non-dimensional parameters corresponding to the ratios of blast duration to the time period of the structure and the velocity of the structure to the particle velocity of the incident blast pressure wave are identified. It is shown that for a given blast pressure wave, the error in the maximum displacement predicted by ignoring FSI effect during structural motion is directly proportional to the ratio of the structure velocity to the particle velocity of the incident blast pressure wave. There is a continuous exchange of energy between the structure and air during the structural motion, which is significant when the structural velocity is significant compared to the particle velocity of incident blast pressure wave. FSI effect become insignificant when the ratio of velocities starts approaching zero.     

Parameterized real-time moment computation on gray images using block techniques

Moments constitute a well-known tool in the field of image analysis and pattern recognition, but they suffer from the drawback of high computational cost. Efforts for the reduction of the required computational complexity have been reported, mainly focused on binary images, but recently some approaches for gray images have been also presented. In this study, we propose a simple but effective approach for the computation of gray image moments. The gray image is decomposed in a set of binary images. Some of these binary images are substituted by an ideal image, which is called “half-intensity” image. The remaining binary images are represented using the image block representation concept and their moments are computed fast using block techniques. The proposed method computes approximated moment values with an error of 2–3% from the exact values and operates in real time (i.e., video rate). The procedure is parameterized by the number m of “half-intensity” images used, which controls the approximation error and the speed gain of the method. The computational complexity is O(kL ²), where k is the number of blocks and L is the moment order.