The DCABES 2009 presentation ppt can be downloaded as follows:

Craig C. Douglas: A Prototype for an Incorrect or Defective Pill Detection Using a Dynamic Data-Driven Application System

Robert Lovas: Integrated Service and Desktop Gridsfor Scientific Computing

Hai Xiang Lin: Trends and Challenges in High Performance Computing

Stefan Vandewalle: Analysis of a Time-Parallel Time-Integration Method

Shiming Xu: Utilizing CUDA for Preconditioned GMRES Solvers

Cheng Jiejing: The Integration and Application of Resources for Distance Education Teaching Based on Distributed Technology

Hyoungseok Chu: Parallel ADI Method for Parabolic Problems on GP-GPU

Hai Xiang ZHAO: A Parallel Statistical Learning Approach to the Prediction of Building Energy Consumption Based on Large Datasets

Mingxi Yang: Fast Signature Scheme for Network Coding

Hongxu Chang: Large-scale Parallel Simulation of High-dimensional American Option Pricing

Jiang Wenqian: The Parallel Models of Coronal Polarization Brightness Calculation

Nicholas Christakis: The Application of Artificial Neural Networks in Engineering and Finance

PENG Hai: A FRACTAL ENCRYPTION ALGORITHM

Fahad Fazal Elahi Guraya: People Tracking via a Modified CAMSHIFT Algorithm

Miloslav Hub: Method of Password Security Evaluation

Qifeng Yang: The Method of Tax Collection and Technical Realization Based on the Third-Party Online Payment Mode

Ping Song: Research on Individual Reputation Management Based on Third-party Online Payment Mode

 

Keynote Speakers of the DCABES 2009

 

Parallel time domain methods

Integrated service and desktop grids for scientific computing

Professor Kacsuk Peter

Coordinator of EDGeS EDGeS - Enabling Desktop Grids for e-Science ( European project with the aim of creating an integrated Grid infrastructure that seamlessly integrates a variety of Desktop Grids with EGEE type of service Grids.)

 

A Prototype for Detecting Defective Pills During Manufacturing

Prof Craig C. Douglas

Distinguished Professor of Mathematics Department, School of Energy Resources University of Wyoming and Senior Research Scientist of Computer Science, Yale University

ABSTRACT Contaminants and impurity formation in pharmaceutical products during manufacturing cause inconvenience and costly recalls. Determining when to adjust a process to control impurity formation, or halt production and destroy end products should be an automatic task using experts’ knowledge, data (audio and chemical spectral footprints), adaptive models and algorithms, and different scales (data and models). Integrated sensing and processing (ISP) optimizes sensing systems that integrate the traditionally independent units of sensing, signal processing, communication and targeting. By employing ISP, computational complexity within traditional sensing system is substantially reduced through determining efficient low-dimensional representations of those sensing problems that were originally posed in high-dimensional settings by traditional sensing architecture. An ISP-based imaging spectrometer produces detector signals directly correlated to desired sample information, obviating the need for post-collection chemometrics and converting data directly to knowledge. We apply ISP techniques to novel acoustic spectral sensors designed and created as part of this project. The ISP is reprogrammable on demand. It uses data libraries that are created offline. The creation is expensive computationally, but it makes the use of the sensor easy. We have to use high performance computing facilities to quickly generate new or modified libraries.

Keywords:Defect detection, manufacturing, integrated sensing and processing, dynamic data-driven application systems, DDDAS, cyber physical systems, CPS, parallel procesing, and algorithms.

 

 

Trends in High Performance Computing

Prof hai xiang Lin

Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology

ABSTRACT In the past 3 decades, high performance computing has undergone a tremendous fast development. The achievement in high performance computing is obtained through the synergy in the development of integrated circuit technology, parallel computing systems and efficient algorithms. Performance of a single processor has been increased by a factor of 106 since the introduction of the first electronic computer in the late 1940s, the application of massively parallel processing adds an increase by another factor of 105, and moreover the development of fast and efficient algorithms further accelerate the solution speed by factor of 106 or more for many large and complex computer applications. The current trends in parallel architectures moving towards multi-cores, much larger number of processors or cores, and heterogeneity pose great challenges to software and tool developers, to algorithm and application programmers.

Keywords: Parallel and distributed computing, Multi-core, GPU, HPC systems and algorithms, technological development in HPC.