National Center For High-performance Computing Research Articles

The Roles of the National Center for High-performance Computing in Projects Involving Health-Care Big Data

<p>生物檢體數位化後將有機會永久保存生物檢體中的資訊與成就資料再利用之最大效益，然而數位化後的生醫巨量資料將仰賴大型的資訊基礎設施使資料服務為之不墜。近年來隨著生醫科技的進展，無論是基因定序或人工智慧技術的應用下，資料的規模，或者是計算規模，與背後的資訊設備需求，早已不是個別研究人員的實驗室資源所可以承受。再者隨著對個人資料保護的要求、注重資訊安全，及尊重資料當事人對自身資料權利表達機會，乃至於資料使用者對於資料分析的方便性與功能完整性的要求，讓生醫資料的運用與處理更仰賴於各項先進資訊技術集結，透過資訊專業建置出符合各界期待的生醫平台，讓生醫資料發揮其最大的效益。</p> <p> </p><p>The digitalization of biosamples offers an opportunity to permanently retain the information contained in biosamples and maximize data reutilization. However, the digitalized biomedical big data require large-scale information infrastructure to sustain relevant services. With the advancement of biomedical technologies, applications involving gene sequencing and artificial intelligence have exceeded a level that individual researchers or laboratories can afford in terms of data volume, computational capacity, and hardware requirements. The demands for personal information protection, information security, right of information self-determination, and the convenience and functional completeness of data analysis have also made biomedical data application and processing more reliant on advanced information technologies. A biomedical platform that meets the expectations of all stakeholders should thus be established through advanced information technologies to extract the maximum potential from biomedical data.</p> <p> </p>

AUTOMATED FORMOSAT IMAGE PROCESSING SYSTEM FOR RAPID RESPONSE TO INTERNATIONAL DISASTERS

FORMOSAT-2, Taiwan’s first remote sensing satellite, was successfully launched in May of 2004 into the Sun-synchronous orbit at 891 kilometers of altitude. With the daily revisit feature, the 2-m panchromatic, 8-m multi-spectral resolution images captured have been used for researches and operations in various societal benefit areas. This paper details the orchestration of various tasks conducted in different institutions in Taiwan in the efforts responding to international disasters. The institutes involved including its space agency-National Space Organization (NSPO), Center for Satellite Remote Sensing Research of National Central University, GIS Center of Feng-Chia University, and the National Center for High-performance Computing. Since each institution has its own mandate, the coordinated tasks ranged from receiving emergency observation requests, scheduling and tasking of satellite operation, downlink to ground stations, images processing including data injection, ortho-rectification, to delivery of image products. With the lessons learned from working with international partners, the FORMOSAT Image Processing System has been extensively automated and streamlined with a goal to shorten the time between request and delivery in an efficient manner. The integrated team has developed an Application Interface to its system platform that provides functions of search in archive catalogue, request of data services, mission planning, inquiry of services status, and image download. This automated system enables timely image acquisition and substantially increases the value of data product. Example outcome of these efforts in recent response to support Sentinel Asia in Nepal Earthquake is demonstrated herein.

AUTOMATED FORMOSAT IMAGE PROCESSING SYSTEM FOR RAPID RESPONSE TO INTERNATIONAL DISASTERS

Abstract. FORMOSAT-2, Taiwan’s first remote sensing satellite, was successfully launched in May of 2004 into the Sun-synchronous orbit at 891 kilometers of altitude. With the daily revisit feature, the 2-m panchromatic, 8-m multi-spectral resolution images captured have been used for researches and operations in various societal benefit areas. This paper details the orchestration of various tasks conducted in different institutions in Taiwan in the efforts responding to international disasters. The institutes involved including its space agency-National Space Organization (NSPO), Center for Satellite Remote Sensing Research of National Central University, GIS Center of Feng-Chia University, and the National Center for High-performance Computing. Since each institution has its own mandate, the coordinated tasks ranged from receiving emergency observation requests, scheduling and tasking of satellite operation, downlink to ground stations, images processing including data injection, ortho-rectification, to delivery of image products. With the lessons learned from working with international partners, the FORMOSAT Image Processing System has been extensively automated and streamlined with a goal to shorten the time between request and delivery in an efficient manner. The integrated team has developed an Application Interface to its system platform that provides functions of search in archive catalogue, request of data services, mission planning, inquiry of services status, and image download. This automated system enables timely image acquisition and substantially increases the value of data product. Example outcome of these efforts in recent response to support Sentinel Asia in Nepal Earthquake is demonstrated herein.

Soma detection in Drosophila brain using Machine Learning

Event Abstract Back to Event Soma detection in Drosophila brain using Machine Learning Guan-Wei He1, Nan-Yow Chen2, Ting-Yuan Wang3, ANN-SHYN CHIANG3, 4 and Yu-Tai Ching1, 5* 1 National Chiao Tung University, Department of Computer Science, Taiwan 2 National Center for High-Performance Computing, Taiwan 3 National Tsing Hua University, Institute of Biotechnology, Taiwan 4 National Tsing Hua University, Brain Research Center, Taiwan 5 National Chiao Tung University, Institute of Biomedical Engineering, Taiwan To compute the neuronal structure in the Drosophila Melanogaster’s brain is important to study the behaviors and gene function. Images of neurons are obtained by three dimensional confocal microscope. Two tasks in computing the neuronal structure are finding the cell-body and construct the neuronal structure. We present an automatic method to determine the cell-body by applying machine learning technique. Data set consists of raw data containing noises. The size of the image is 1024x1024x150. There could be more than one soma in the volume and the number of soma is not known. Data set consists of 100 volume images. These images are divided into 5-fold for cross validation. Proposed method starts with some candidate voxels that could be the center of the soma. These points are obtained by the distance transform of the image of the neuron. A regression model is then constructed based on the features that are voxel intensity, coordinate of the voxel, and density and balance factor around the candidate voxel. The result shows that sensitivity is more than 80% and true positive rate is near 90%.

FlyDriver: A Connectomic Approach to Find Specific Drivers of Target Neuron in the FlyCircuit

Event Abstract Back to Event FlyDriver: A Connectomic Approach to Find Specific Drivers of Target Neuron in the FlyCircuit Ting-Yuan Wang1, Yen-Jen Lin2, Hsuan-Wen Lin1, Chao-Chun Chuang3, Meng-Hsuan Chiang1 and Ann-Shyn Chiang1, 2, 4, 5* 1 National Tsing Hua University, Institute of Biotechnology, Taiwan 2 National Tsing Hua University, Brain Research Center, Taiwan 3 National Center for High-performance Computing, Taiwan 4 Academia Sinica, Genomics Research Center, Taiwan 5 University of California at San Diego, Kavli Institute for Brain and Mind, United States We collected about 4000 driver expression images of Drosophila melanogaster, which included Gal4, LexA, flipase and spilt-Gal4 system for manipulating neuronal circuit function. All confocal microscopy images was archived into a database, named FlyDriver. FlyDriver was followed the same platform of FlyCircuit. Therefore, we can compare single neuron image with driver image. Here, we used image-matching algorithm on both neuron-driver image pairs and driver-driver image pairs between FlyCircuit and FlyDriver, which amount over the 100 million image pairs was been calculated. The overlap information of neuron-driver image pairs could help user to find a specific driver with a target neuron. In addition, user could also find similar driver with overlap information of driver-driver image pairs. We have illustrated the utility of these image-matching data for identifying the putative driver expressed target neuron and finding the specific driver from a set of neuron images. For some expression pattern that did not exist in one single driver, we could use the overlap information to predict expression patterns that comes from the intersection region from two driver images. As a result, FlyDriver provided a web interface that allows users to find the driver with different criterion. This will facilitate finding a specific driver to understanding the function of neuronal circuit. Keywords: neuron image, driver image, image databse, driver database, Image matching algorithm Conference: Neuroinformatics 2015, Cairns, Australia, 20 Aug - 22 Aug, 2015. Presentation Type: Poster, to be considered for oral presentation Topic: Computational neuroscience Citation: Wang T, Lin Y, Lin H, Chuang C, Chiang M and Chiang A (2015). FlyDriver: A Connectomic Approach to Find Specific Drivers of Target Neuron in the FlyCircuit. Front. Neurosci. Conference Abstract: Neuroinformatics 2015. doi: 10.3389/conf.fnins.2015.91.00011 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 02 May 2015; Published Online: 05 Aug 2015. * Correspondence: Prof. Ann-Shyn Chiang, National Tsing Hua University, Institute of Biotechnology, Hsinchu city, 300, Taiwan, aschiang@life.nthu.edu.tw Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Ting-Yuan Wang Yen-Jen Lin Hsuan-Wen Lin Chao-Chun Chuang Meng-Hsuan Chiang Ann-Shyn Chiang Google Ting-Yuan Wang Yen-Jen Lin Hsuan-Wen Lin Chao-Chun Chuang Meng-Hsuan Chiang Ann-Shyn Chiang Google Scholar Ting-Yuan Wang Yen-Jen Lin Hsuan-Wen Lin Chao-Chun Chuang Meng-Hsuan Chiang Ann-Shyn Chiang PubMed Ting-Yuan Wang Yen-Jen Lin Hsuan-Wen Lin Chao-Chun Chuang Meng-Hsuan Chiang Ann-Shyn Chiang Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

A web based remote visualization architecture for big neuroimaging data

Event Abstract Back to Event A web based remote visualization architecture for big neuroimaging data Chao-Chun Chuang1* 1 National center for high-performance computing, Taiwan As recent neuroimaging research indicates, 3D visualization is helpful for understanding complex neuron structure. It also can help scientists share their visualization neuroimaging data and make their representation easier to understand [1][2]. In most cases, scientists downloaded these big neuroimaging data and then visualized on a local desktop environments with 3D visualization tools. However, in recent years, higher resolution datasets in the neuroimaging research continue massive growth with higher experimental resolving power. It becomes inconvenient to download these big neuroimaging data without enough network bandwidth and difficult to display these images in user client without enough computer resources. Thus, we demo a web-based remote visualization architecture for these big neuroimaging data, and take FlyCircuit 3D viewer as an example. FlyCircuit 3D viewer uses advanced 3-layer web technologies, web server (WebSockets, Canvas), visualization server (VNC and VirtualGL) and user client (HTML5) for interactive online 3D surface, skeleton and volumetric neuroimaging data in any web browser environments without requiring any browser plugin[2][3][4][5]. The first layer, web server communicates with the second layer visualization server by WebSockets and Canvas. Then third layer user client can view 3D neuroimaging data from web page constructed by HTML5. Thus, with powerful visualization server, users can easily interactive the 3D visualization neuron imaging by web browser in a lightweight computer resource. Finally, this visualization architecture can handle massive 3D neuronal image data collected in experiments from different research groups as well as manage bio-images with deeper neurological insight. Figure 1

Balance between efficiency and stability in a neural circuit model of the Drosophila brain

Event Abstract Back to Event Balance between efficiency and stability in a neural circuit model of the Drosophila brain Cheng-Te Wang1, Yu-Chi Huang1 and Chung-Chuan Lo1* 1 National Tsing Hua University, Institute of Systems Neuroscience, Taiwan Fruit fly (Drosophila melanogaster) is becoming a promising model animal in computational neuroscience for its small brain size, complex cognitive behavior and abundant data from genes to circuits. Although much has being learned about the functions of individual sensory systems, how do they operated and integrated at the brain-wide level in Drosophila remains poorly understood. To provide a platform for studying computation of the fly nervous system, we initiated the Flysim project with a goal to construct a full-brain neural circuit model with a single-neuron resolution based on the data obtained from the flycircuit database. Although containing only ~28,000 neurons (account for 22% of the fly brain) at the current stage, our fly brain model already exhibits unexpectedly rich dynamics. Due to the strongly recurrent excitation, the brain model is inherently unstable and epilepsy-like activity frequently arose from the baseline state. We further found that the epilepsy-like activity cannot be suppressed by solely strengthening the inhibitory synapses but can be eliminated by implementing short-term depression. However, depressed synapses strongly attenuate the propagation of signals and lead to an inefficient neural circuit. Therefore, a functioning nervous system requires fine balance between stability and efficiency. Additional analyses revealed that the epilepsy-like activity originates from a group of specific brain regions that are functionally less understood but seem to play roles in integrating signals from different sensory modules. By a large-scale screening through the inhibitory system of the brain model, we further discovered that the epilepsy-like activity can be suppressed by a small number of inhibitory neurons if they are manually activated. Interestingly, the epilepsy-suppressing ability of these neurons does not come from their inhibitory strength, but from their unique innervating patterns over the excitatory system. In conclusion, despite being in an early stage of the model development, our fly brain model has already shown its strength in providing insights into brain-wide neural dynamics which may not be revealed by analyzing the random or simple small-world neural networks. Acknowledgements This work is supported by the Ministry of Science and Technology and by the Ministry of Education, Taiwan. We thank the National Center for High-performance Computing for providing computational resources and Dr. Ann-Shyn Chiang for helpful comments. Keywords: Drosophila, Epilepsy, network stability, fruit fly, balanced networks, Efficiency, short-term depression, short-term synaptic plasticity, Inhibitory Control Conference: Neuroinformatics 2015, Cairns, Australia, 20 Aug - 22 Aug, 2015. Presentation Type: Poster, to be considered for oral presentation Topic: Computational neuroscience Citation: Wang C, Huang Y and Lo C (2015). Balance between efficiency and stability in a neural circuit model of the Drosophila brain. Front. Neurosci. Conference Abstract: Neuroinformatics 2015. doi: 10.3389/conf.fnins.2015.91.00035 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 07 Apr 2015; Published Online: 05 Aug 2015. * Correspondence: Dr. Chung-Chuan Lo, National Tsing Hua University, Institute of Systems Neuroscience, Hsinchu city, US & Canada only, 300, Taiwan, cclo@life.nthu.edu.tw Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Cheng-Te Wang Yu-Chi Huang Chung-Chuan Lo Google Cheng-Te Wang Yu-Chi Huang Chung-Chuan Lo Google Scholar Cheng-Te Wang Yu-Chi Huang Chung-Chuan Lo PubMed Cheng-Te Wang Yu-Chi Huang Chung-Chuan Lo Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Pervasive brain monitoring and data sharing based on multi-tier distributed computing and linked data technology.

EEG-based Brain-computer interfaces (BCI) are facing basic challenges in real-world applications. The technical difficulties in developing truly wearable BCI systems that are capable of making reliable real-time prediction of users' cognitive states in dynamic real-life situations may seem almost insurmountable at times. Fortunately, recent advances in miniature sensors, wireless communication and distributed computing technologies offered promising ways to bridge these chasms. In this paper, we report an attempt to develop a pervasive on-line EEG-BCI system using state-of-art technologies including multi-tier Fog and Cloud Computing, semantic Linked Data search, and adaptive prediction/classification models. To verify our approach, we implement a pilot system by employing wireless dry-electrode EEG headsets and MEMS motion sensors as the front-end devices, Android mobile phones as the personal user interfaces, compact personal computers as the near-end Fog Servers and the computer clusters hosted by the Taiwan National Center for High-performance Computing (NCHC) as the far-end Cloud Servers. We succeeded in conducting synchronous multi-modal global data streaming in March and then running a multi-player on-line EEG-BCI game in September, 2013. We are currently working with the ARL Translational Neuroscience Branch to use our system in real-life personal stress monitoring and the UCSD Movement Disorder Center to conduct in-home Parkinson's disease patient monitoring experiments. We shall proceed to develop the necessary BCI ontology and introduce automatic semantic annotation and progressive model refinement capability to our system.

The Flysim project – persistent simulation and real-time visualization of fruit fly whole-brain spiking neural network model

The Flysim project – persistent simulation and real-time visualization of fruit fly whole-brain spiking neural network model

ASNeuPI – An Algorithm for Skeleton-based Neuronal Polarity Identification

Event Abstract Back to Event ASNeuPI – An Algorithm for Skeleton-based Neuronal Polarity Identification Yi-Hsuan Lee1, Yen-Nan Lin1, 2 and Chung-Chuan Lo1, 2* 1 Institute of system neuroscience, National Tsing Hua University, Taiwan 2 Brain Research Center, Taiwan The direction of signal transmission is crucial for functions of neural networks. Therefore, the direction of signal flow, which is regulated by neuronal polarity, should be included when we analyze neural networks. However, the biochemical method used to identify neuronal polarity is time-consuming and may not be an appropriate strategy for analyzing large-scale neural networks. To address this problem, we proposed an algorithm for skeleton-based neuronal polarity identification (ASNeuPI). In ASNeuPI, we first morphologically divide a neuron into several substructures and for each substructure we extract seventeen morphological features. Next, K-nearest classifier (KNNC) is applied for identifying the most influential feature combination that correlates with the polarity of the training dataset. Finally, we perform linear discriminant analysis (LDA) to generate an optimal axis that provides highest accuracy for polarity identification. The optimal axis is then used to identify polarity of neurons in the testing set. We tested this method on neurons innervating protocerebral bridge (PCB) or medulla (MED) in Drosophila. The neuron skeletons were extracted from data obtained from Brain Research Center, National Tsing Hua University, Taiwan. On average, the polarity of more than 85% terminal points in a neuron could be correctly identified. We tested the maximum performance of ASNeuPI on a clean dataset constructed by manually removing artificial branches resulting from noise in raw images. We found that the average accuracy reaches 95% in the best case. Our results show that, as a computer-based semi-automatic procedure, ASNeuPI provides quick polarity identification and is particularly suitable for analyzing large-scale data. Figure 1 Figure 2 Acknowledgements This work is supported by National Science Council grant #NSC 101-2311-B-007 -008 -MY3 and by Aim for the Top University Project of the Ministry of Education, Taiwan. We also thank National Center for High-performance Computing for providing FlyCircuit database. References Chiang, A.-S., Lin, C.-Y., Chuang, C.-C., Chang, H.-M., Hsieh, C.-H., Yeh, C.-W., Shih, C.-T., Wu, J.-J., Wang, G.-T., Chen, Y.-C., et al. (2010). Three-Dimensional Reconstruction of Brain-wide Wiring Networks in Drosophila at Single-Cell Resolution. Curr Biol. Available at: http://www.ncbi.nlm.nih.gov/pubmed/21129968 [Accessed December 16, 2010]. Keywords: neuronal polarity, Drosophila, neural imaging, neuron reconstruction, neural networks, Axons, Dendrites Conference: Neuroinformatics 2013, Stockholm, Sweden, 27 Aug - 29 Aug, 2013. Presentation Type: Poster Topic: General neuroinformatics Citation: Lee Y, Lin Y and Lo C (2013). ASNeuPI – An Algorithm for Skeleton-based Neuronal Polarity Identification. Front. Neuroinform. Conference Abstract: Neuroinformatics 2013. doi: 10.3389/conf.fninf.2013.09.00008 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 08 Apr 2013; Published Online: 11 Jul 2013. * Correspondence: Dr. Chung-Chuan Lo, Institute of system neuroscience, National Tsing Hua University, Hsinchu, 30013, Taiwan, cclo@life.nthu.edu.tw Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Yi-Hsuan Lee Yen-Nan Lin Chung-Chuan Lo Google Yi-Hsuan Lee Yen-Nan Lin Chung-Chuan Lo Google Scholar Yi-Hsuan Lee Yen-Nan Lin Chung-Chuan Lo PubMed Yi-Hsuan Lee Yen-Nan Lin Chung-Chuan Lo Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Molecular Dynamics Simulation of Thin Film Evaporation of Lennard-Jones Liquid

Evaporation of a thin liquid film is of significant fundamental importance for both science and engineering applications. This work investigates the evaporation of a thin liquid argon layer into vacuum employing molecular dynamics simulation based on the Lennard-Jones potential. The simulation results demonstrate that the net evaporation rate of an ultra-thin liquid film into vacuum in a closed system may be modeled by the balance of evaporation and condensation based on the Schrage model. The evaporation/condensation coefficient and the non-Maxwellian factor may thus be evaluated. As for the open system, the simulation results demonstrate a constant evaporation rate for each leakage probability. The corresponding evaporation heat transfer coefficient is very high and increases with increase in leakage percentage. Such a high heat transfer coefficient demonstrates very high heat transfer capability of evaporation from an ultra-thin liquid film. This work was supported by the National Science Council of Taiwan, ROC, under the contract of NSC90-2212-E-007-103. The computer program employed in this work was modified from the basic code developed by Professor S. Maruyama of the Department of Mechanical Engineering, the University of Tokyo. The authors would also like to acknowledge the valuable discussion with Dr. M.H. Hsieh of the Institute of Nuclear Energy Research, Taiwan, ROC. The program was executed using the supercomputers provided by the National Center for High-Performance Computing, Taiwan, ROC. This manuscript was rewritten based on authors' papers presented in the ASME Summer Heat Transfer Conference, Las Vegas, 2003, for closed system simulations and some results from the First International Symposium on Micro & Nano Technology, Hawaii, 2004, for open system simulations.

Biotechnology: the next engine of growth for Taiwan's economy? The Taiwanese government is investing heavily in biotech infrastructure and researchers in an effort to repeat the country's success with IT technologies.

On a recent visit to the Genome Research Center (GRC) at Taiwan's National Yang‐Ming University in Taipei, its director, Kwang‐Jen Hsiao, pointed to a brightly highlighted line in a list of contributors to the Human Genome Project (HGP). ‘If we [were one of the official HGP consortium members],’ he said, ‘that is where we should be.’ The list—reproduced and altered to include the GRC—was from a page in the historic February 2001 issue of the journal Nature . GRC's contribution, 12 megabases of work mostly on Chromosome 4, was about two‐thirds down the page. Taiwan's role in the HGP—hidden but significant—is reflective of the country's position in the world‐wide biotechnology community. But the assumption that it is only a minor player belies the fervour of recent efforts to build a research infrastructure and to develop its biotechnology sector. Indeed, biotechnology has moved into Taiwan's science parks and onto the national agenda. It has gained the commitment of the government, and expectations are high that this will become the next engine of Taiwanese economic growth. In 1997, the value of new biotechnology industries in Taiwan was estimated at $14.7 billion Taiwan dollars (€440 million) and the Ministry of Economic Affairs predicts that it will grow to NT$80 billion (€2.5 billion) by 2005. With growing interest from the government, businesses and venture capital firms, hopes are high in Taiwan that the factors which led to the island's strength in semiconductor manufacture and IT‐related industries will also translate to biotechnology. But there are many differences between the two industries and the learning curve may prove to be steep. In addition, not only must Taiwan play ‘catch‐up’ within a fairly narrow window of time and against strong international competition, but the island's size and limited experience—23 million people and a high‐tech history that spans …

NETWORKED COLLABORATIVE ENVIRONMENT FOR HYDRO-ENGINEERING

International Journal of Computational Engineering ScienceVol. 02, No. 04, pp. 557-567 (2001) No AccessNETWORKED COLLABORATIVE ENVIRONMENT FOR HYDRO-ENGINEERINGSHIN-JYE LIANG, JIUNN-HORNG LEE, SHIH-HUNG CHEN and JR-HUANG SHIAUSHIN-JYE LIANGDepartment of Hydraulic Engineering, Feng Chia University, Taichung, Taiwan, R.O.CCorresponding author: 100 Wenhwa Road, Taichung, Taiwan, R.O.C.; Tel: +886-3-5776085; Fax: +886-3-5773538. Search for more papers by this author , JIUNN-HORNG LEENational Center for High-Performance Computing, Hsinchu, Taiwan, R.O.C Search for more papers by this author , SHIH-HUNG CHENNational Center for High-Performance Computing, Hsinchu, Taiwan, R.O.C Search for more papers by this author and JR-HUANG SHIAUNational Center for High-Performance Computing, Hsinchu, Taiwan, R.O.C Search for more papers by this author https://doi.org/10.1142/S1465876301000441Cited by:1 Next AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail AbstractEngineering projects are increasingly becoming more complex and have to be carried out in close cooperation among several experts from different disciplines and locations. This has resulted from a rapid progress in communication, computing, visualization, and storage technology. Research and applications of distributed computing, visualization and networked collaboration, therefore, is becoming an important subject. We present three ongoing distributed collaborative systems at the National Center for High-performance Computing in Taiwan: (1) A collaborative visualization environment which supports distributed and interactive visualization and collaboration in the three-dimensional virtual world, (2) A collaborative web browser system that creates an HTML document shared environment for supporting remote conferencing, and (3) An Internet-based distributed analysis environment that is designed for distributed simulation and visualization. We have explored and examined various computer tools and communication technologies for the development of the networked collaborative systems. Our endeavor sheds some light on the future development and applications of an efficient networked collaborative infrastructure which supports distributed computing, presentation and visualization, data and information management, documentation, and conferencing.Keywords:information technologysoftware integrationnetworked analysisvisualization and collaborationhydroinformatics FiguresReferencesRelatedDetailsCited By 1Technologies and Services in Support of Virtual WorkplacesAlan McCord and Morell D. Boone1 Jan 2008 Recommended Vol. 02, No. 04 Metrics History Keywordsinformation technologysoftware integrationnetworked analysisvisualization and collaborationhydroinformaticsPDF download

Taiwan's National Center for High-Performance Computing

The author discusses Taiwan's National Centre for High Performance Computing. It is a multivendor environment based around a IBM RS/600D SP distributed memory system. Customers are served through two high-bandwidth networks: an academic network and the National Information Infrastructure.