Optimization of active region for 1.3-µm GalnAsP compressive-strain multiple-quantum-well ridge waveguide laser diodes

We report on the optimization of Ga_0.27In_0.73As_0.67P_0.33/Ga_0.11In_0.89As_0.24P_0.76 compressive-strain multiple-quantum-well (CS-MQW) grown by low-pressure metalorganic chemical vapor deposition for 1.3-μm ridge-waveguide laser diodes (LDs). The structural and optical properties are characterized by doublecrystal x-ray diffraction and photoluminescence (PL) measurements, respectively. The optimum thicknesses of the well, barrier, and waveguide layer of the active region are 4 nm, 10 nm, and 100 nm, respectively. The GaInAsP/GaInAsP CS-MQW as-cleaved LDs with the optimum active region, a 3.5-μm-width ridge, and a 900-μm-cavity length exhibit the threshold current density of 1.09 kA/cm², a differential quantum efficiency of 30%, a characteristic temperature of 60 K, a maximum operating temperature up to 75°C, and a redshift rate of 0.30 nm/°C.     

A genetic fuzzy agent using ontology model for meeting scheduling system

A Genetic Fuzzy Agent (GFA) using the ontology model for Meeting Scheduling System (MSS) is presented in this paper. The ontology model includes the Fuzzy Meeting Scheduling Ontology (FMSO) and the Fuzzy Personal Ontology (FPO) that can support to construct the knowledge base of the GFA. The FMSO is utilized to record and describe the meeting scheduling domain knowledge for the GFA. In addition, we implement a FMSO editor for generating the Web Ontology Language, OWL, that will be utilized by the GFA. Furthermore, the GFA will infer the suitable meeting time slots based on the ontology model. Moreover, it also adjusts the FMSO and FPO based on the results of the genetic learning mechanism for the next meeting. The experimental results show that our approach can effectively work for MSS.     

A Simple Decomposition Method for Support Vector Machines

The decomposition method is currently one of the major methods for solving support vector machines. An important issue of this method is the selection of working sets. In this paper through the design of decomposition methods for bound-constrained SVM formulations we demonstrate that the working set selection is not a trivial task. Then from the experimental analysis we propose a simple selection of the working set which leads to faster convergences for difficult cases. Numerical experiments on different types of problems are conducted to demonstrate the viability of the proposed method.     

Investigation of a solid-state detector for advanced computed tomography

Utilization of solid-state detectors for computed tomography (CT) has been the focus of many studies. Previous phantom and clinical experiments have shown that one of the important performance parameters for the solid-state detector is the primary speed and afterglow. In this paper, we present a detailed investigation on the signal decay characteristics of the HiLight (GE Medical Systems, Milwaukee, WI) scintillating detector. The detector primary speed and afterglow are modeled by a multiexponential function and fully characterized by a set of time constants and relative strengths. The sensitivity of these parameters to X-ray photon energy, detector aging, and radiation exposure is then established and analyzed. No statistically significant variation is observed in these parameters due to changes in the above external variables. The impact of various decay time constants on CT image quality, such as spatial resolution, noise, and artifacts, is subsequently illustrated with computer simulations and phantom experiments. Finally, an algorithmic correction scheme is derived to compensate for detector afterglow. The correction scheme employs a recursive filter to remove adverse effects of the detector decay on image quality. Experimental results have shown that the correction scheme successfully restores system spatial resolution, produces a more homogeneous noise pattern, and eliminates ring-band image artifacts due to detector afterglow. The effectiveness and robustness of the correction scheme are demonstrated by extensive phantom and clinical experiments.     

Tomographic reconstruction for tilted helical multislice CT

One of the most recent technical advancements in computed tomography (CT) is the introduction of multislice CT (MCT). Because multiple detector rows are used for data acquisition, MCT offers higher volume coverage, faster scan speed, and reduced X-ray tube loading. Recognizing its unique data-sampling pattern, several image reconstruction algorithms were developed. These algorithms have been shown to be adequate in producing clinically acceptable images. Recent studies, however, have revealed that the image quality of MCT can be significantly degraded when helical data are acquired with a tilted gantry. The degraded image quality has rendered this feature unacceptable for clinical usage. In this paper, we first present a detailed investigation on the cause of the image quality degradation. An analytical model is derived to provide a mathematical basis for correction. Several compensation schemes are subsequently presented, and a detailed performance comparison is provided in terms of spatial resolution, noise, computation efficiency, and image artifacts.     

Genetic Algorithm for Optimization of Infrastructure Investment Under Time-Resource Constraints

Abstract:   This work shows the infrastructure investment decision is essentially a 0-1, nonlinear, multiobjective knapsack problem. It argues that, without making substantial simplification on some of the practical considerations, a conventional mathematical optimization approach may not be suitable for solving the problem. Further, the use of explicit heuristics may sometimes be unwieldy dependent on the system parameters. An innovative approach based on the concept of genetic algorithms is proposed in the work to tackle the nonlinearity in the decision problem. Based on the experimental results, the proposed approach is capable of producing good quality results. The key success feature of the proposed approach resides in the unique and innovative modeling of system parameters by genetic coding in the algorithms .     

Telechelic elastomers

Telechelic elastomers, polymers with reactive terminal groups, can be prepared in emulsion and solution systems. Examples of characterization of the polymers and preparation of a mercapto telechelic copolymer in an emulsion system and of mercapto-, hydroxy-, and aziridinyl-telechelic elastomers in solution systems are given. The elastomers were cured in peroxide or sulfur-accelerator formulations. The telechelic polymers exhibited enhanced stress-strain and dynamic properties in comparision to those of the controls. In tread formulations, outstanding properties were obtained for the mercapto-and azieridinyl-telechelic butadiene-styrene copolymers.     

Sampling variability of the area under the ROC-curve and of d'e.

By computer simulation, random samples were drawn from underlying Gaussian, uniform, and negative exponential distributions which differed in terms of their mean and variance. The conditional probability of an observation of a given magnitude, or higher, was computed for each of 2 distributions. Resulting conditional probabilities for the 2 distributions trace out a receiver operating characteristic (ROC) curve. The area under the ROC-curve, which provides a distribution-free measure of performance, was calculated over a wide range of possible performances. It is shown that the sampling variability of the area measure is dependent upon its mean value and is relatively independent of the complex of conditions which lead to the given mean value. A similar conclusion is reached for the sampling variability of the intersection of the ROC-curve with the negative diagonal. This measure leads to an estimate of the sampling variability of the measure d'e, an index of O performance, often employed in the signal detectability approach to performance measurement. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

High performance data compression method with pattern matching for biomedical ECG and arterial pulse waveforms

Biomedical waveforms, such as electrocardiogram (ECG) and arterial pulse, always possess a lot of important clinical information in medicine and are usually recorded in a long period of time in the application of telemedicine. Due to the huge amount of data, to compress the biomedical waveform data is vital. By recognizing the strong similarity and correlation between successive beat patterns in biomedical waveform sequences, an efficient data compression scheme mainly based on pattern matching is introduced in this paper. The waveform codec consists mainly of four units: beat segmentation, beat normalization, two-stage pattern matching and template updating and residual beat coding. Three different residual beat coding methods, such as Huffman/run-length coding, Huffman/run-length coding in discrete cosine transform domain, and vector quantization, are employed. The simulation results show that our compression algorithms achieve a very significant improvement in the performances of compression ratio and error measurement for both ECG and pulse, as compared with some other compression methods.