Defect mode in periodic and quasiperiodic one-dimensional photonic structures

The properties of one-dimensional periodic and quasiperiodic photonic crystals with a defect layer have been investigated. Transfer matrix method (TMM) has been used throughout this study. For periodic photonic crystals, results demonstrate the independence of the defect mode frequency on the defect layer while the defect mode transmission coefficient varies with the position of the defect layer position. On the other hand, defect mode frequency is not that responsive to the index of refraction of the defect layer. The quality factor of the defect mode has been studied as a function of the defect layer position as well as its thickness. For quasiperiodic photonic crystals, the frequency of the defect mode is very sensitive to the defect layer position as well as its thickness. An enhancement of the quality factor of the defect mode has been observed. This study may be valuable in designing optical devices and it may also provide a more accurate method to measure the index of refraction.     

Evaluation of the Form Birefringence and Other Structural Parameters Due to Thermal Annealing for Nylon 6 Fibers

In the present work nylon 6 fibers were annealed at 155±1C at different times and allowed to cool down in the oven to room temperature (27±1C). A Pluta polarizing interference microscope was used for measuring the changes in the optical parameters. From the optical and density parameters the optical orientation values of F ₂(), F ₄(), F ₆() and F _av were calculated. Also, the dielectric constant, dielectric susceptibility, molar refractivity, segment anisotropy, optical stress coefficient, optical configuration and the form birefringence were evaluated. The segment anisotropy and deformational thermal stress was evaluated. Relationships between the measured and calculated parameters are given for illustration. The mechanism of structural variations for nylon 6 fiber due to the annealing process is discussed, and structural details for opto thermal parameters are explained. The present study throws light on the changes due to thermal annealing conditions for nylon 6 fibers. Microinterferograms and curves are given for illustration.     

A Survey on IP Watermarking Techniques

Intellectual property (IP) block reuse is essential for facilitating the design process of system-on-a-chip. Sharing IP designs poses significant high security risks. Recently, digital watermarking emerged as a candidate solution for copyright protection of IP blocks. In this paper, we survey and classify different techniques used for watermarking IP designs. To this end, we defined several evaluation criteria, which can also be used as a benchmark for new IP watermarking developments. Furthermore, we established a comprehensive set of requirements for future IP watermarking techniques.     

A multiple objective grouping genetic algorithm for assembly line design

The purpose of this paper is to describe some of the main problems concerning assembly line design. The focus will be on the following steps: (1) the input data preparation, (2) the elaboration of the logical layout of the line, which consists in the distribution of operations among stations along the line and an assignment of resources to the different stations, (3) finally the mapping phase using a simulation package to check the obtained results. This work presents a new method to tackle the hybrid assembly line design, dealing with multiple objectives. The goal is to minimize the total cost of the line by integrating design (station space, cost, etc.) and operation issues (cycle time, precedence constraints, availability, etc.). This paper also presents in detail a very promising approach to solve multiple objective problems. It is a multiple objective grouping genetic algorithm hybridized with the multicriteria decision-aid method PROMETHEE II. An approach to deal with users preferences in design problems is also introduced. The essential concepts adopted by the method are described and its application to an industrial case study is presented.     

Fast computation of spatial selections and joins using graphics hardware

Spatial database operations are typically performed in two steps. In the filtering step, indexes and the minimum bounding rectangles (MBRs) of the objects are used to quickly determine a set of candidate objects. In the refinement step, the actual geometries of the objects are retrieved and compared to the query geometry or each other. Because of the complexity of the computational geometry algorithms involved, the CPU cost of the refinement step is usually the dominant cost of the operation for complex geometries such as polygons. Although many run-time and pre-processing-based heuristics have been proposed to alleviate this problem, the CPU cost still remains the bottleneck. In this paper, we propose a novel approach to address this problem using the efficient rendering and searching capabilities of modern graphics hardware. This approach does not require expensive pre-processing of the data or changes to existing storage and index structures, and is applicable to both intersection and distance predicates. We evaluate this approach by comparing the performance with leading software solutions. The results show that by combining hardware and software methods, the overall computational cost can be reduced substantially for both spatial selections and joins. We integrated this hardware/software co-processing technique into a popular database to evaluate its performance in the presence of indexes, pre-processing and other proprietary optimizations. Extensive experimentation with real-world data sets show that the hardware-accelerated technique not only outperforms the run-time software solutions but also performs as well if not better than pre-processing-assisted techniques.     

Fatigue Life Evaluation of Concrete Bridge Deck Slabs Reinforced with Glass FRP Composite Bars

Since bridge deck slabs directly sustain repeated moving wheel loads, they are one of the most bridge elements susceptible to fatigue failure. Recently, glass fiber-reinforced polymer (FRP) composites have been widely used as internal reinforcement for concrete bridge deck slabs as they are less expensive compared to the other kinds of FRPs (carbon and aramid). However, there is still a lack of information on the performance of FRP–reinforced concrete elements subjected to cyclic fatigue loading. This research is designed to investigate the fatigue behavior and fatigue life of concrete bridge deck slabs reinforced with glass FRP bars. A total of five full-scale deck slabs were constructed and tested under concentrated cyclic loading until failure. Different reinforcement types (steel and glass FRP), ratios, and configurations were used. Different schemes of cyclic loading (accelerated variable amplitude fatigue loading) were applied. Results are presented in terms of deflections, strains in concrete and FRP bars, and crack widths at different levels of cyclic loading. The results showed the superior fatigue performance and longer fatigue life of concrete bridge deck slabs reinforced with glass FRP composite bars.     

Control and Navigation Framework for Quadrotor Helicopters

This paper presents the development of a nonlinear quadrotor simulation framework together with a nonlinear controller. The quadrotor stabilization and navigation problems are tackled using a nested loops control architecture. A nonlinear Backstepping controller is implemented for the inner stabilization loop. It asymptotically tracks reference attitude, altitude and heading trajectories. The outer loop controller generates the reference trajectories for the inner loop controller to reach the desired waypoint. To ensure boundedness of the reference trajectories, a PD controller with a saturation function is used for the outer loop. Due to the complexity involved in controller development and testing, a simulation framework has been developed. It is based on the Gazebo 3D robotics simulator and the Open Dynamics Engine (ODE) library. The framework can effectively facilitate the development and validation of controllers. It has been released and is available at Gazebo quadrotor simulator (2012).     

A Fast and Effective Multiple Kernel Clustering Method on Incomplete Data

Multiple kernel clustering is an unsupervised data analysis method that has been used in various scenarios where data is easy to be collected but hard to be labeled. However, multiple kernel clustering for incomplete data is a critical yet challenging task. Although the existing absent multiple kernel clustering methods have achieved remarkable performance on this task, they may fail when data has a high value-missing rate, and they may easily fall into a local optimum. To address these problems, in this paper, we propose an absent multiple kernel clustering (AMKC) method on incomplete data. The AMKC method first clusters the initialized incomplete data. Then, it constructs a new multiple-kernel-based data space, referred to as K-space, from multiple sources to learn kernel combination coefficients. Finally, it seamlessly integrates an incomplete-kernel-imputation objective, a multiple-kernel-learning objective, and a kernel-clustering objective in order to achieve absent multiple kernel clustering. The three stages in this process are carried out simultaneously until the convergence condition is met. Experiments on six datasets with various characteristics demonstrate that the kernel imputation and clustering performance of the proposed method is significantly better than state-of-the-art competitors. Meanwhile, the proposed method gains fast convergence speed.