Accurate interferometric system to perform the substrate focusing and alignment in laser writing processes

Abstract

A simple, low cost system useful in laser writing processes to set the focal spot on the substrate surface with micrometre precision is described. Evaluation of its sensitivity is also presented. The lack of essential critical adjustments accounts for the utility of this system and its performance.     

Mechanical properties of a modified acrylic bone cement with etoxytriethyleneglycol monomethacrylate

With the aim of improving some of the disadvantages of the acrylic bone cements, an acrylic bone cement based on polymethyl methacrylate has been modified by substituting different quantities, up to 20%, of the monomer methyl methacrylate (MMA) with the same amount of ethoxytriethyleneglycol monomethacrylate (TEG). The addition of this new monomer decreased noticeably the maximum temperature and increased both setting and working times. Mechanical testing revealed that the introduction of TEG gave rise to a less fragile bone cement by increasing slightly the total deformation without any change in the rest of the tensile parameters.     

Multivariate curve resolution applied to the analysis and resolution of two-dimensional [1H,15N] NMR reaction spectra

Multivariate curve resolution is proposed for the study of complex chemical reactions monitored by two-dimensional (2D) NMR spectroscopy. In particular, in this work, multivariate curve resolution is applied to the study of the reaction between (15)N-labeled cisplatin and the amino acid-nucleotide hybrid (Phac-Met-linker-p(5')dG). At several stages of the reaction, 2D [(1)H,(15)N] HSQC NMR spectra were acquired and stored in data matrices. In a first step, multivariate curve resolution was applied to analyze individually each one of these 2D spectra, allowing the resolution of the corresponding (1)H and (15)N one-dimensional correlation spectra. In a second step, the whole set of 2D spectra recorded along the reaction were simultaneously analyzed by multivariate curve resolution, allowing the resolution of the kinetic concentration profiles and of the pure 2D NMR spectra of each of the species detected along the reaction. Results finally obtained confirmed previously postulated reaction mechanisms involving the existence of two monofunctional adducts and of two bifunctional adducts, with the structure of one of them not completely resolved.     

An Analysis of Edge Detection by Using the Jensen-Shannon Divergence

This work constitutes a theoretical study of the edge-detection method by means of the Jensen-Shannon divergence, as proposed by the authors. The overall aim is to establish formally the suitability of the procedure of edge detection in digital images, as a step prior to segmentation. In specific, an analysis is made not only of the properties of the divergence used, but also of the method's sensitivity to the spatial variation, as well as the detection-error risk associated with the operating conditions due to the randomness of the spatial configuration of the pixels. Although the paper deals with the procedure based on the Jensen-Shannon divergence, some problems are also related to other methods based on local detection with a sliding window, and part of the study is focused to noisy and textured images.     

Distributed data clustering in sensor networks

Low overhead analysis of large distributed data sets is necessary for current data centers and for future sensor networks. In such systems, each node holds some data value, e.g., a local sensor read, and a concise picture of the global system state needs to be obtained. In resource-constrained environments like sensor networks, this needs to be done without collecting all the data at any location, i.e., in a distributed manner. To this end, we address the distributed clustering problem, in which numerous interconnected nodes compute a clustering of their data, i.e., partition these values into multiple clusters, and describe each cluster concisely. We present a generic algorithm that solves the distributed clustering problem and may be implemented in various topologies, using different clustering types. For example, the generic algorithm can be instantiated to cluster values according to distance, targeting the same problem as the famous k-means clustering algorithm. However, the distance criterion is often not sufficient to provide good clustering results. We present an instantiation of the generic algorithm that describes the values as a Gaussian Mixture (a set of weighted normal distributions), and uses machine learning tools for clustering decisions. Simulations show the robustness, speed and scalability of this algorithm. We prove that any implementation of the generic algorithm converges over any connected topology, clustering criterion and cluster representation, in fully asynchronous settings.