Search results for: Nhan Do

Authors: Van Nhan Nguyen, Harald Holone

Abstract:

Over the past few years, a lot of research has been conducted to bring Automatic Speech Recognition (ASR) into various areas of Air Traffic Control (ATC), such as air traffic control simulation and training, monitoring live operators for with the aim of safety improvements, air traffic controller workload measurement and conducting analysis on large quantities controller-pilot speech. Due to the high accuracy requirements of the ATC context and its unique challenges, automatic speech recognition has not been widely adopted in this field. With the aim of providing a good starting point for researchers who are interested bringing automatic speech recognition into ATC, this paper gives an overview of possibilities and challenges of applying automatic speech recognition in air traffic control. To provide this overview, we present an updated literature review of speech recognition technologies in general, as well as specific approaches relevant to the ATC context. Based on this literature review, criteria for selecting speech recognition approaches for the ATC domain are presented, and remaining challenges and possible solutions are discussed.

Keywords: automatic speech recognition, asr, air traffic control, atc

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Authors: Ting Ye, Nhan Phan-Thien, Chwee Teck Lim, Lina Peng, Huixin Shi

Abstract:

In biofluid flow systems, often the flow problems of fluids of complex structures, such as the flow of red blood cells (RBCs) through complex capillary vessels, need to be considered. In this paper, we aim to apply a particle-based method, Smoothed Dissipative Particle Dynamics (SDPD), to simulate the motion and deformation of RBCs in complex micro-tubes. We first present the theoretical models, including SDPD model, RBC-fluid interaction model, RBC deformation model, RBC aggregation model, and boundary treatment model. After that, we show the verification and validation of these models, by comparing our numerical results with the theoretical, experimental and previously-published numerical results. Finally, we provide some simulation cases, such as the motion and deformation of RBCs in rectangular, cylinder, curved, bifurcated, and constricted micro-tubes, respectively.

Keywords: aggregation, deformation, red blood cell, smoothed dissipative particle dynamics

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Authors: Hoang Van Ngoc, Nguyen Vu Nhan, Nguyen Quang Bau

Abstract:

The light-effect in cylindrical quantum wire with an infinite potential for the case of electrons, optical phonon scattering, is studied based on the quantum kinetic equation. The density of the direct current in a cylindrical quantum wire by a linearly polarized electromagnetic wave, a DC electric field, and an intense laser field is calculated. Analytic expressions for the density of the direct current are studied as a function of the frequency of the laser radiation field, the frequency of the linearly polarized electromagnetic wave, the temperature of system, and the size of quantum wire. The density of the direct current in cylindrical quantum wire with an infinite potential for the case of electrons – optical phonon scattering is nonlinearly dependent on the frequency of the linearly polarized electromagnetic wave. The analytic expressions are numerically evaluated and plotted for a specific quantum wire, GaAs/GaAsAl.

Keywords: the light–effect, cylindrical quantum wire with an infinite potential, the density of the direct current, electrons-optical phonon scattering

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Authors: Nhan N. T. Ton, Anh T. N. Dao, Kouichirou Katou, Toshiaki Taniike

Abstract:

Facile electron-hole recombination and the broad band gap are two major drawbacks of titanium dioxide (TiO₂) when applied in visible-light photocatalysis. Hybridization of TiO₂ with graphene is a promising strategy to lessen these pitfalls. Recently, there have been many reports on the synthesis of TiO₂/graphene nanocomposites, in most of which graphene oxide (GO) was used as a starting material. However, the reduction of GO introduced a large number of defects on the graphene framework. In addition, the sensitivity of titanium alkoxide to water (GO usually contains) significantly obstructs the uniform and controlled growth of TiO₂ on graphene. Here, we demonstrate a novel technique to synthesize TiO₂/graphene nanocomposites without the use of GO. Graphene dispersion was obtained through the chemical exfoliation of graphite in titanium tetra-n-butoxide with the aid of ultrasonication. The dispersion was directly used for the sol-gel reaction in the presence of different catalysts. A TiO₂/reduced graphene oxide (TiO₂/rGO) nanocomposite, which was prepared by a solvothermal method from GO, and the commercial TiO₂-P25 were used as references. It was found that titanium alkoxide afforded the graphene dispersion of a high quality in terms of a trace amount of defects and a few layers of dispersed graphene. Moreover, the sol-gel reaction from this dispersion led to TiO₂/graphene nanocomposites featured with promising characteristics for visible-light photocatalysts including: (I) the formation of a TiO₂ nano layer (thickness ranging from 1 nm to 5 nm) that uniformly and thinly covered graphene sheets, (II) a trace amount of defects on the graphene framework (low ID/IG ratio: 0.21), (III) a significant extension of the absorption edge into the visible light region (a remarkable extension of the absorption edge to 578 nm beside the usual edge at 360 nm), and (IV) a dramatic suppression of electron-hole recombination (the lowest photoluminescence intensity compared to reference samples). These advantages were successfully demonstrated in the photocatalytic decomposition of methylene blue under visible light irradiation. The TiO₂/graphene nanocomposites exhibited 15 and 5 times higher activity than TiO₂-P25 and the TiO₂/rGO nanocomposite, respectively.

Keywords: chemical exfoliation, photocatalyst, TiO₂/graphene, sol-gel reaction

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Authors: David J. Foran, Nhan Do, Samuel Ajjarapu, Wenjin Chen, Tahsin Kurc, Joel H. Saltz

Abstract:

The large-scale data and computational requirements of investigators throughout the clinical and research communities demand an informatics infrastructure that supports both existing and new investigative and translational projects in a robust, secure environment. In some subspecialties of medicine and research, the capacity to generate data has outpaced the methods and technology used to aggregate, organize, access, and reliably retrieve this information. Leading health care centers now recognize the utility of establishing an enterprise-wide, clinical data warehouse. The primary benefits that can be realized through such efforts include cost savings, efficient tracking of outcomes, advanced clinical decision support, improved prognostic accuracy, and more reliable clinical trials matching. The overarching objective of the work presented here is the development and implementation of a flexible Intelligent Retrieval and Interrogation System (IRIS) that exploits the combined use of computational imaging, genomics, and data-mining capabilities to facilitate clinical assessments and translational research in oncology. The proposed System includes a multi-modal, Clinical & Research Data Warehouse (CRDW) that is tightly integrated with a suite of computational and machine-learning tools to provide insight into the underlying tumor characteristics that are not be apparent by human inspection alone. A key distinguishing feature of the System is a configurable Extract, Transform and Load (ETL) interface that enables it to adapt to different clinical and research data environments. This project is motivated by the growing emphasis on establishing Learning Health Systems in which cyclical hypothesis generation and evidence evaluation become integral to improving the quality of patient care. To facilitate iterative prototyping and optimization of the algorithms and workflows for the System, the team has already implemented a fully functional Warehouse that can reliably aggregate information originating from multiple data sources including EHR’s, Clinical Trial Management Systems, Tumor Registries, Biospecimen Repositories, Radiology PAC systems, Digital Pathology archives, Unstructured Clinical Documents, and Next Generation Sequencing services. The System enables physicians to systematically mine and review the molecular, genomic, image-based, and correlated clinical information about patient tumors individually or as part of large cohorts to identify patterns that may influence treatment decisions and outcomes. The CRDW core system has facilitated peer-reviewed publications and funded projects, including an NIH-sponsored collaboration to enhance the cancer registries in Georgia, Kentucky, New Jersey, and New York, with machine-learning based classifications and quantitative pathomics, feature sets. The CRDW has also resulted in a collaboration with the Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC) at the U.S. Department of Veterans Affairs to develop algorithms and workflows to automate the analysis of lung adenocarcinoma. Those studies showed that combining computational nuclear signatures with traditional WHO criteria through the use of deep convolutional neural networks (CNNs) led to improved discrimination among tumor growth patterns. The team has also leveraged the Warehouse to support studies to investigate the potential of utilizing a combination of genomic and computational imaging signatures to characterize prostate cancer. The results of those studies show that integrating image biomarkers with genomic pathway scores is more strongly correlated with disease recurrence than using standard clinical markers.

Keywords: clinical data warehouse, decision support, data-mining, intelligent databases, machine-learning.

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