Brainwave Classification for Character-Writing Application Using EMD-Based GMM and KELM Approaches

A brainwave classification, which does not involve any limb movement and stimulus for character-writing applications, benefits impaired people, in terms of practical communication, because it allows users to command a device/computer directly via electroencephalogram signals. In this paper, we propose a new framework based on Empirical Mode Decomposition (EMD) features along with the Gaussian Mixture Model (GMM) and Kernel Extreme Learning Machine (KELM)-based classifiers. For this purpose, firstly, we introduce EMD to decompose EEG signals into Intrinsic Mode Functions (IMFs), which actually are used as the input features of the brainwave classification for the character-writing application. We hypothesize that EMD along with the appropriate IMF is quite powerful for the brainwave classification, in terms of character applications, because of the wavelet-like decomposition without any down sampling process. Secondly, by getting motivated with shallow learning classifiers, we can provide promising performance for the classification of binary classes, GMM and KELM, which are applied for the learning of features along with the brainwave classification. Lastly, we propose a new method by combining GMM and KELM to fuse the merits of different classifiers. Moreover, the proposed methods are validated by using the volunteer-independent 5-fold cross-validation and accuracy as a standard measurement. The experimental results showed that EMD with the proper IMF achieved better results than the conventional discrete wavelet transform (DWT) feature. Moreover, we found that the EMD feature along with the GMM/KELM-based classifier provides the average accuracy of 77.40% and 80.10%, respectively, which could perform better than the conventional methods where we use DWT along with the artificial neural network classifier in order to get the average accuracy of 80.60%. Furthermore, we obtained the improved performance by combining GMM and KELM, i.e., average accuracy of 80.60%. These outcomes exhibit the usefulness of the EMD feature combining with GMM and KELM based classifiers for the brainwave classification in terms of the Character-Writing application, which do not require any limb movement and stimulus.     

Nonlinear Finite Element Analysis of a FRP-Strengthened Reinforced Concrete Bridge

Three-dimensional nonlinear finite element (FE) models are developed to examine the structural behavior of the Horsetail Creek Bridge strengthened by fiber-reinforced polymers (FRPs). A sensitivity study is performed varying bridge geometry, precracking load, strength of concrete, and stiffness of the soil foundation to establish a FE model that best represents the actual bridge. Truck loadings are applied to the FE bridge model at different locations, as in an actual bridge test. Comparisons between FE model predictions and field data are made in terms of strains in the beams for various truck load locations. It is found that all the parameters examined can potentially influence the bridge response and are needed for selection of the optimal model which predicts the magnitudes and trends in the strains accurately. Then, using the optimal model, performance evaluation of the bridge based on scaled truck and mass-proportional loadings is conducted. Each loading type is gradually increased until failure occurs. Structural responses are compared for strengthened and unstrengthened bridge models to evaluate the FRP retrofit. The models predict a significant improvement in structural performance due to the FRP retrofit.     

Data collection and restoration for heterogeneous process migration

This study presents a practical solution for data collection and restoration to migrate a process written in high‐level stack‐based languages such as C and Fortran over a network of heterogeneous computers. We first introduce a logical data model, namely the Memory Space Representation (MSR) model, to recognize complex data structures in process address space. Then, novel methods are developed to incorporate the MSR model into a process, and to collect and restore data efficiently. We have implemented prototype software and performed experiments on different programs. Experimental and analytical results show that: (1) a user‐level process can be migrated across different computing platforms; (2) semantic information of data structures in the process's memory space can be correctly collected and restored; (3) costs of data collection and restoration depend on the complexity of the MSR graph in the memory space and the amount of data involved; and (4) the implantation of the MSR model into the process is not a decisive factor of incurring execution overheads. With appropriate program analysis, we can practically achieve low overhead. Copyright © 2002 John Wiley & Sons, Ltd.     

The Earth System Grid: Supporting the Next Generation of Climate Modeling Research

Understanding the Earth's climate system and how it might be changing is a preeminent scientific challenge. Global climate models are used to simulate past, present, and future climates, and experiments are executed continuously on an array of distributed supercomputers. The resulting data archive, spread over several sites, currently contains upwards of 100 TB of simulation data and is growing rapidly. Looking toward mid-decade and beyond, we must anticipate and prepare for distributed climate research data holdings of many petabytes. The Earth System Grid (ESG) is a collaborative interdisciplinary project aimed at addressing the challenge of enabling management, discovery, access, and analysis of these critically important datasets in a distributed and heterogeneous computational environment. The problem is fundamentally a Grid problem. Building upon the Globus toolkit and a variety of other technologies, ESG is developing an environment that addresses authentication, authorization for data access, large-scale data transport and management, services and abstractions for high-performance remote data access, mechanisms for scalable data replication, cataloging with rich semantic and syntactic information, data discovery, distributed monitoring, and Web-based portals for using the system.     

100‐period, 1.23‐eV bandgap InGaAs/GaAsP quantum wells for high‐efficiency GaAs solar cells: toward current‐matched Ge‐based tandem cells

Major challenges for InGaAs/GaAsP multiple quantum well (MQW) solar cells include both the difficulty in designing suitable structures and, because of the strain‐balancing requirement, growing high‐quality crystals. The present paper proposes a comprehensive design principle for MQWs that overcomes the trade‐off between light absorption and carrier transport that is based, in particular, on a systematical investigation of GaAsP barrier effects on carrier dynamics that occur for various barrier widths and heights. The fundamental strategies related to structure optimization are as follows: (i) acknowledging that InGaAs wells should be thinner and deeper for a given bandgap to achieve both a higher absorption coefficient for 1e‐1hh transitions and a lower compressive strain accumulation; (ii) understanding that GaAs interlayers with thicknesses of just a few nanometers effectively extend the absorption edge without additional compressive strain and suppress lattice relaxation during growth; and (iii) understanding that GaAsP barriers should be thinner than 3 nm to facilitate tunneling transport and that their phosphorus content should be minimized while avoiding detrimental lattice relaxation. After structural optimization of 1.23‐eV bandgap quantum wells, a cell with 100‐period In_0.30GaAs(3.5 nm)/GaAs(2.7 nm)/GaAsP_0.40(3.0 nm) MQWs exhibited significantly improved performance, showing 16.2% AM 1.5 efficiency without an anti‐reflection coating, and a 70% internal quantum efficiency beyond the GaAs band edge. When compared with the GaAs control cell, the optimized cell showed an absolute enhancement in AM 1.5 efficiency, and 1.22 times higher efficiency with 38% current enhancement with an AM 1.5 cut‐off using a 665‐nm long‐pass filter, thus indicating the strong potential of MQW cells in Ge‐based 3‐J tandem devices. Copyright © 2013 John Wiley & Sons, Ltd.