Reconstruction of the refractive index profile of planar waveguides using ray tracing analysis

We propose a method for refractive index profiling based on measuring coordinates and angles of laser beams passing across the waveguide layer. Calculations are performed by solving an integral equation using new global optimization methods.

A study on reliability in graph discovery

A critical question in data mining is that can we always trust what discovered by a data mining system unconditionally? The answer is obviously not. If not, when can we trust the discovery then? What are the factors that affect the reliability of the discovery? How do they affect the reliability of the discovery? These are some interesting questions to be investigated.

In this paper we will firstly provide a definition and the measurements of reliability, and analyse the factors that affect the reliability. We then examine the impact of model complexity, weak links, varying sample sizes and the ability of different learners to the reliability of graphical model discovery. The experimental results reveal that (1) the larger sample size for the discovery, the higher reliability we will get; (2) the stronger a graph link is, the easier the discovery will be and thus the higher the reliability it can achieve; (3) the complexity of a graph also plays an important role in the discovery. The higher the complexity of a graph is, the more difficult to induce the graph and the lower reliability it would be.

Polyhedral studies for minimum-span graph labelling with integer distance constraints

This paper studies the polytope of the minimum-span graph labelling problems with integer distance constraints (DC-MSGL). We first introduce a few classes of new valid inequalities for the DC-MSGL defined on general graphs and briefly discuss the separation problems of some of these inequalities. These are the initial steps of a branch-and-cut algorithm for solving the DC-MSGL. Following that, we present our polyhedral results on the dimension of the DC-MSGL polytope, and that some of the inequalities are facet defining, under reasonable conditions, for the polytope of the DC-MSGL on triangular graphs.

Planar receiver placement for unique emitter localization for indoor applications

This paper investigates two independent approaches to verify the necessary and sufficient condition to guarantee a unique solution for a passive emitter localization system based on time difference of arrival measurements from an
array of sensors.

Planar waveguide refractive index profile reconstruction using global optimization

The approach taken here of reconstruction of the refractive index profile of planar waveguides involves solving a non-linear integral equation with Tikhonov regularization. Using global optimization with the new cutting angle and discrete gradient methods has yielded an acceptable reconstruction, even in the presence of significant noise in the data.

Picture this : a simple graph that reveals much ado about research

Funnel graphs provide a simple, yet highly effective, means to identify key features of an empirical literature. This paper illustrates the use of funnel graphs to detect publication selection bias, identify the existence of genuine empirical effects and discover potential moderator variables that can help to explain the wide variation routinely found among reported research findings. Applications include union–productivity effects, water price elasticities, common currency-trade effects, minimum-wage employment effects, efficiency wages and the price elasticity of prescription drugs.

Advanced graph mining methods for protein analysis

As one of the primary substances in a living organism, protein defines the character of each cell by interacting with the cellular environment to promote the cell’s growth and function [1]. Previous studies on proteomics indicate that the functions of different proteins could be assigned based upon protein structures [2,3]. The knowledge on protein structures gives us an overview of protein fold space and is helpful for the understanding of the evolutionary principles behind structure. By observing the architectures and topologies of the protein families, biological processes can be investigated more directly with much higher resolution and finer detail. For this reason, the analysis of protein, its structure and the interaction with the other materials is emerging as an important problem in bioinformatics. However, the determination of protein structures is experimentally expensive and time consuming, this makes scientists largely dependent on sequence rather than more general structure to infer the function of the protein at the present time. For this reason, data mining technology is introduced into this area to provide more efficient data processing and knowledge discovery approaches.

Unlike many data mining applications which lack available data, the protein structure determination problem and its interaction study, on the contrary, could utilize a vast amount of biologically relevant information on protein and its interaction, such as the protein data bank (PDB) [4], the structural classification of proteins (SCOP) databases [5], CATH databases [6], UniProt [7], and others. The difficulty of predicting protein structures, specially its 3D structures, and the interactions between proteins as shown in Figure 6.1, lies in the computational complexity of the data. Although a large number of approaches have been developed to determine the protein structures such as ab initio modelling [8], homology modelling [9] and threading [10], more efficient and reliable methods are still greatly needed.

In this chapter, we will introduce a state-of-the-art data mining technique, graph mining, which is good at defining and discovering interesting structural patterns in graphical data sets, and take advantage of its expressive power to study protein structures, including protein structure prediction and comparison, and protein-protein interaction (PPI). The current graph pattern mining methods will be described, and typical algorithms will be presented, together with their applications in the protein structure analysis.

The rest of the chapter is organized as follows: Section 6.2 will give a brief introduction of the fundamental knowledge of protein, the publicly accessible protein data resources and the current research status of protein analysis; in Section 6.3, we will pay attention to one of the state-of-the-art data mining methods, graph mining; then Section 6.4 surveys several existing work for protein structure analysis using advanced graph mining methods in the recent decade; finally, in Section 6.5, a conclusion with potential further work will be summarized.

Characterization of supported cylinder-planar germanium waveguide sensors with synchrotron Infrared radiation

Cylinder-planar Ge waveguides are being developed as evanescent-wave sensors for chemical microanalysis. The only non-planar surface is a cylinder section having a 300-mm radius of curvature. This confers a symmetric taper, allowing for direct coupling into and out of the waveguide's 1-mm² end faces while obtaining multiple reflections at the central <30-μm-thick sensing region. Ray-optic calculations indicate that the propagation angle at the central minimum has a strong nonlinear dependence on both angle and vertical position of the input ray. This results in rather inefficient coupling of input light into the off-axis modes that are most useful for evanescent-wave absorption spectroscopy. Mode-specific performance of the cylinder-planar waveguides has also been investigated experimentally. As compared to a blackbody source, the much greater brightness of synchrotron-generated infrared (IR) radiation allows a similar total energy throughput, but restricted to a smaller fraction of the allowed waveguide modes. However, such angle-selective excitation results in a strong oscillatory interference pattern in the transmission spectra. These spectral oscillations are the principal technical limitation on using synchrotron radiation to measure evanescent-wave absorption spectra with the thin waveguides.

Symmetrically tapered <30-micron-thick quasi-planar Ge waveguides as chemical sensors for microanalysis

Symmetrically tapered planar IR waveguides have been fabricated by starting with a ZnS coated concave piece of single-crystal Ge, embedding it in an epoxide resin as a supporting substrate, and then grinding and polishing a planar surface until the thickness at the taper minimum is <30 μm. Such tapering is expected to enhance a waveguide's sensitivity as an evanescent wave sensor by maximizing the amount of evanescent wave energy present at the thinnest part of the waveguide. As predicted by theory, the surface sensitivity, i.e., the absorbance signal per molecule in contact with the sensing region, increases with decreasing thickness of the tapered region even while the total energy throughput decreases. The signal-to-noise ratio obtained depends very strongly on the quality of the polished surfaces of the waveguides. The surface sensitivity is superior to that obtained with a commercial Ge attenuated total reflection (ATR) accessory for several types of sample, including thin films (<10 ng) and small volumes (<1 μL) of volatile solvents. By using the waveguides, light-induced structural changes in the protein bacteriorhodopsin were observable using samples as small as ∼50 pmol (∼1 μg). In addition, the waveguide sensors can reveal the surface compositions on a single human hair, pointing to their promise as a tool for forensic fiber analysis.

Graph matching with subdivision surfaces for texture synthesis on surfaces

Existing texture synthesis-from-example strategies for polygon meshes typically make use of three components: a multi-resolution mesh hierarchy that allows the overall nature of the pattern to be reproduced before filling in detail; a matching strategy that extends the synthesized texture using the best fit from a texture sample; and a transfer mechanism that copies the selected portion of the texture sample to the target surface. We introduce novel alternatives for each of these components. Use of p2-subdivision surfaces provides the mesh hierarchy and allows fine control over the surface complexity. Adaptive subdivision is used to create an even vertex distribution over the surface. Use of the graph defined by a surface region for matching, rather than a regular texture neighbourhood, provides for flexible control over the scale of the texture and allows simultaneous matching against multiple levels of an image pyramid created from the texture sample. We use graph cuts for texture transfer, adapting this scheme to the context of surface synthesis. The resulting surface textures are realistic, tolerant of local mesh detail and are comparable to results produced by texture neighbourhood sampling approaches.

A family of planar parallel manipulators

A family of planar parallel manipulators is investigated and some novel members are proposed. The common feature of the studied manipulators is that the rotation axes of the actuated arms coincide. This feature makes it possible to rotate the whole arm system an infinite number of revolutions around the center of the manipulator. The result is a large workspace in relation to the footprint. Both 2- and 3-DOF variants are presented and the suitability of this family of manipulators for kinematic analysis is demonstrated. Thus, different methods to find optimal manipulability with respect to platform positioning and rotation have been analyzed.

Reliable graph discovery

A critical question in data mining is that can we always trust what discovered by a data mining system unconditionally? The answer is obviously not. If not, when can we trust the discovery then? What are the factors that affect the reliability of the discovery? How do they affect the reliability of the discovery? These are some interesting questions to be investigated. In this chapter we will firstly provide a definition and the measurements of reliability, and analyse the factors that affect the reliability. We then examine the impact of model complexity, weak links, varying sample sizes and the ability of different learners to the reliability of graphical model discovery. The experimental results reveal that (1) the larger sample size for the discovery, the higher reliability we will get; (2) the stronger a graph link is, the easier the discovery will be and thus the higher the reliability it can achieve; (3) the complexity of a graph also plays an important role in the discovery. The higher the complexity of a graph is, the more difficult to induce the graph and the lower reliability it would be. We also examined the performance difference of different discovery algorithms. This reveals the impact of discovery process. The experimental results show the superior reliability and robustness of MML method to standard significance tests in the recovery of graph links with small samples and weak links.