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MILCOM, Military Communications Conference

作者： Shahrzad Shirazipourazad Pavel Ghosh Arunabha Sen Computer Science and Engineering Program School of Computing Informatics and Decision System Engineering Arizona State University USA

The U.S. Air Force is currently in the process of building an Airborne Network (AN), where the nodes are a set of heterogeneous, highly mobile, Airborne Networking Platforms (ANPs) - such as satellites, airplanes and unmanned aerial vehicles. Mobility pattern of nodes in a mobile network has significant impact on the coverage and connectivity properties of the network. The level of reliability needed for continuous operation of an AN may be difficult to achieve through a completely infrastructure-less mobile ad hoc networks. In an earlier paper, we proposed an architecture for an AN where a set of ANPs form the backbone of the AN. In this architecture, the ANPs may be viewed as mobile base stations with predictable and well-structured flight paths and the combat aircrafts on a mission as mobile clients. In this paper we consider the AN scenario where a part of the network might not be operational due to enemy attack and/or jamming. We consider faults that are spatially correlated (or region-based), that is faults due to an enemy attack are confined to a region. The goal is to design a robust AN so that no matter which region in the deployment area fails and at what time, the surviving nodes of the network will remain connected and be able to communicate with each other. We propose an algorithm that finds the minimum transmission range necessary to ensure network connectivity irrespective of location of the fault region and the time of the failure.

关键词： Mobile communication Computer architecture Mobile computing Reliability Orbits Aircraft Network topology

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A Clonal Selection Algorithm for the container stacking problem

A Clonal Selection Algorithm for the container stacking prob...

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World Congress on Nature & Biologically Inspired Computing (NaBIC)

作者： Luiz Antonio Carraro Leandro Nunes de Castro Computer and Informatics Faculty & Graduate Program in Electrical Engineering Mackenzie University Sao Paulo Brazil

Due to increasing demand, container terminals face the challenges of increasing its service capacity and minimize the time of ships loading and unloading. The assignment of storage locations for reshuffled containers is a problem to the efficiency of container terminals operations, as a wrong assignment may result in several future reshuffles. This paper proposes a meta-heuristic based on the Clonal Selection Algorithm to minimize reshuffle when retrieving containers from a stack. The model competitiveness in accuracy and time are established by extensive numerical experiments comparing with an existing Integer program model. The preliminary results obtained here suggest the proposed model is a promising optimization tool for the container stacking problem.

关键词： Containers Stacking Cloning Optimization Immune system Loading Computational modeling

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Collaboration model of software development

Collaboration model of software development

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2011 International Conference on Electrical engineering and informatics, ICEEI 2011

作者： Kusumasari, Tien Fabrianti Supriana, Iping Surendro, Kridanto Sastramihardja, Husni Information System Program Study STMIK im Bandung Jl.Jakarta No.79 Bandung Indonesia School of Electrical Engineering and Informatics Bandung Institute of Technology Jl. Ganessa No.10 Bandung Indonesia

ISBN: (纸本)9781457707520

Software development is a series of processes activity involving more than one person, especially in medium to large software projects. Each activity of software development is carried out by various combination of different individual. Collaboration occurs in every phase of the process software development. The right team collaboration method is one of the important things in success software development. This research is focused to develop collaboration model of software development to meet time schedule, budget, scope, and software *** collaboration model of software development is generated from survey of collaboration method (about face to face and virtual collaboration) and software development methodology especially agile methodology. The direct collaboration was used in the software specification processes, and combination (with a direct and virtual) collaboration was used during software design, implementation, and validation. Team coordination in every process of software development was done with face to face method. The same artifacts were used in this collaboration model with some collaboration software tools. Combination between face to face and virtual communication was used in this model. The model is implemented in small case software development project. During the software development, some measurement is carried out. The measurement included the measurement of project completion time, cost, software specification, and software quality. The software is developed in time schedule (two months), cost under budged, and meet specification. In addition, the software has built a fairly good quality. Thus, this collaboration model good use in software development to achieved accuracy of schedule, cost, scope, and quality. © 2011 IEEE.

关键词： Software design

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Basic Issues in Vector High Dimensional Model Representation

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AIP Conference Proceedings 2011年第1期1389卷 1143-1146页

作者： Burcu Tunga Metin Demiralp aIstanbul Technical University Faculty of Computer and Informatics Maslak 34469 Istanbul Turkey bIstanbul Technical University Informatics Institute Computational Science and Engineering Program Maslak 34469 Istanbul Turkey

A multivariate modelling may involve a set of multivariate functions. A vector valued function structure can be used to mathematically express the given problem and each multivariate function can be considered as an element of this vector. This work aims to construct a new approach representing the elements of this vector structure in terms of less‐variate functions to reduce the computational complexity. For this purpose, a new method based on the plain High Dimensional Model Representation (HDMR) philosophy is developed. The basic concepts of this method and several illustrative numerical implementations are given here.

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SYSTEMS BIOLOGY IN BARLEY POWDERY MILDEW INTERACTIONS

SYSTEMS BIOLOGY IN BARLEY POWDERY MILDEW INTERACTIONS

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The 13th International Cereal Rusts and Powdery Mildews Conference(第13届国际禾谷类锈病与白粉病大会)

作者： Roger WISE Priyanka SURANA Greg FUERST Ruo XU Dan NETTLETON Divya MISTRY Julie DICKERSON Lin WANG Tom BRUTNELL Department of Plant Pathology and Microbiology Center for Plant Responses to Environmental StressesIowa State UniversityAmesIowa 50011USA Department of Statistics Iowa State UniversityAmesIowa 50011USA Bioinformatics and Computational Biology Graduate ProgramIowa State UniversityAmesIowa 50011USA Bioinformatics and Computational Biology Graduate ProgramIowa State UniversityAmesIowa 50011USA Bioinformatics and Computational Biology Graduate Program Iowa State UniversityAmesIowa 50011USA Department of Plant Pathology and MicrobiologyCenter for Plant Responses to Environmental StressesIowa State UniversityAmesIowa 50011USA Crop and Insect Genetics Genomicsand Informatics Research UnitU.S.Department of Agriculture-Agricultural Research ServiceIowa State UniversityAmesIowa 50011USA Department of Plant Pathology and Microbiology Center for Plant Responses to Environmental StressesIowa State UniversityAmesIowa 50011USA Department of StatisticsIowa State UniversityAmesIowa 50011USA Bioinformatics and Computational Biology Graduate ProgramIowa State UniversityAmesIowa 50011USA Bioinformatics and Computational Biology Graduate Program Iowa State UniversityAmesIowa 50011USA Crop and Insect GeneticsGenomicsand Informatics Research UnitU.S.Department of Agriculture-Agricultural Research ServiceIowa State UniversityAmesIowa 50011USA Department of Electrical and Computer Engineering Iowa State UniversityAmesIowa 50011USA Boyce Thompson Institute for Plant ResearchCornell UniversityIthacaNY

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Beyond connectivity - new metrics to evaluate robustness of networks

Beyond connectivity - new metrics to evaluate robustness of ...

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IEEE Workshop on High Performance Switching and Routing

作者： Sujogya Banerjee Shahrzad Shirazipourazad Pavel Ghosh Arunabha Sen Computer Science and Engineering Program School of Computing Informatics and Decision System Engineering Arizona State University Tempe AZ USA

Robustness or fault-tolerance capability of a network is an important design parameter in both wired and wireless networks. Connectivity of a network is traditionally considered to be the primary metric for evaluation of its fault-tolerance capability. However, connectivity κ(G) (for random faults) or region-based connectivity κ R (G) (for spatially correlated or region-based faults, where the faults are confined to a region R) of a network G, does not provide any information about the network state, (i.e., whether the network is connected or not) once the number of faults exceeds κ(G) or κ R (G). If the number of faults exceeds κ(G) or κ R (G), one would like to know, (i) the number of connected components into which G decomposes, (ii) the size of the largest connected component, (iii) the size of the smallest connected component. In this paper, we introduce a set of new metrics that computes these values. We focus on one particular metric called region-based component decomposition number (RBCDN), that measures the number of connected components in which the network decomposes once all the nodes of a region fail. We study the computational complexity of finding RBCDN of a network. In addition, we study the problem of least cost design of a network with a target value of RBCDN. We show that the optimal design problem is NP-complete and present an approximation algorithm with a performance bound of O(log K + 4log n), where n denotes the number of nodes in the graph and K denotes a target value of RBCDN. We evaluate the performance of our algorithm by comparing it with the performance of the optimal solution. Experimental results demonstrate that our algorithm produces near optimal solution in a fraction of time needed to find an optimal solution.

关键词： Measurement Layout Approximation algorithms Algorithm design and analysis Polynomials Robustness Complexity theory

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An Approach Using Parallel Architecture to Storage DICOM Images in Distributed File System

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Journal of Physics: Conference Series 2012年第1期341卷

作者： Tiago S Soares Thiago C Prado M A R Dantas Douglas D J de Macedo Michael A Bauer Informatics and Statistic Department Federal University of Santa Catarina Florianpolis Brazil Post-Graduate Program of Knowledge Engineering and Management Federal University of Santa Catarina Florianpolis Brazil Department of Computer Science University of Western Ontario UWO London Canada

Telemedicine is a very important area in medical field that is expanding daily motivated by many researchers interested in improving medical applications. In Brazil was started in 2005, in the State of Santa Catarina has a developed server called the CyclopsDCMServer, which the purpose to embrace the HDF for the manipulation of medical images (DICOM) using a distributed file system. Since then, many researches were initiated in order to seek better performance. Our approach for this server represents an additional parallel implementation in I/O operations since HDF version 5 has an essential feature for our work which supports parallel I/O, based upon the MPI paradigm. Early experiments using four parallel nodes, provide good performance when compare to the serial HDF implemented in the CyclopsDCMServer.

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On the feasibility of predicting radiological observations from computational imaging features of liver lesions in CT scans

On the feasibility of predicting radiological observations f...

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2011 1st IEEE International Conference on Healthcare informatics, Imaging and Systems Biology, HISB 2011

作者： Gimenez, Francisco Xu, Jiajing Liu, Yi Liu, Tiffany Ting Beaulieu, Christopher F. Rubin, Daniel L. Napel, Sandy Biomedical Informatics Training Program Stanford University School of Medicine Stanford CA United States Department of Electrical Engineering Stanford University Stanford CA United States Department of Radiology Stanford University Stanford CA United States

ISBN: (纸本)9780769544076

We aim to predict radiological observations using computationally-derived imaging features extracted from CT images. Our dataset consists of 79 portal venous phase liver CT images containing lesions identified and annotated by a radiologist using a controlled vocabulary of 76 semantic terms. Computationally-derived features were extracted describing intensity, texture, shape, and edge sharpness. Linear discriminative analysis, logistic regression and LASSO were explored to predict the radiological observations using computational features. The approach was evaluated by leave one out cross-validation. Informative radiological observations such as lesion enhancement, hyper vascular attenuation, and homogeneous retention were discovered to be well-predicted by computational features. By exploiting relationships between computable and semantic features, this approach could lead to more accurate and efficient radiology reporting. © 2011 IEEE.

关键词： Forecasting

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ESICM LIVES 2016: part two

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Intensive Care Medicine Experimental 2016年第1期4卷 205-404页

作者： S. Sivakumar F. S. Taccone K. A. Desai C. Lazaridis M. Skarzynski M. Sekhon W. Henderson D. Griesdale L. Chapple A. Deane L. Williams R. Strickland K. Lange D. Heyland M. Chapman M. J. Rowland P. Garry J. Westbrook R. Corkill C. A. Antoniades K. T. Pattinson G. Fatania A. J. Strong R. B. Myers C. M. Jermaine C. S. Robertson C. G. Rusin J. Hofmeijer L. Sondag M. C. Tjepkema-Cloostermans A. Beishuizen F. H. Bosch M. J. A. M. van Putten L. Carteron C. Patet D. Solari M. Oddo M. A. Ali C. Dias R. Almeida A. Vaz-Ferreira J. Silva E. Monteiro A. Cerejo A. P. Rocha A. A. Elsayed A. M. Abougabal B. N. Beshey K. M. Alzahaby S. Pozzebon A. Blandino Ortiz S. Cristallini O. Lheureux A. Brasseur J. L. Vincent J. Creteur M. Hravnak K. Yousef Y. Chang E. Crago R. M. Friedlander S. A. Abdelmonem S. A. Tahon T. A. Helmy H. S. Meligy F. Puig I. Dunn-Siegrist J. Pugin S. Gupta D. Govil S. Srinivasan S. J. Patel J. K. N A. Gupta D. S. Tomar M. Shafi R. Harne D. P. Arora N. Talwar S. Mazumdar E. E. Papakrivou D. Makris E. Manoulakas B. Tsolaki B. Karadodas E. Zakynthinos I. Palacios Garcia A. Diaz Martin V. Sanchez Encinares M. Pachón Ibañez J. Garnacho Montero G. Labrador T. Cebrero Cangueiro V. Poulose J. Koh J. W. Kam H. Yeter A. Kara O. Aktepe A. Topeli I. Tsolakoglou G. Intas P. Stergiannis A. A. Kolaros E. Chalari E. Athanasiadou A. Martika G. Fildisis V. Faivre C. Mengelle B. Favier D. Payen A. Poppe M. S. Winkler E. Mudersbach J. Schreiber M. L. Wruck E. Schwedhelm S. Kluge C. Zöllner T. Tavladaki A. M. Spanaki H. Dimitriou E. Kondili C. Choulaki E. Meleti D. Kafetzopoulos D. Georgopoulos G. Briassoulis A. García-de la Torre M. V. de la Torre-Prados T. Tsvetanova-Spasova P. Nuevo-Ortega C. Rueda-Molina A. Fernández-Porcel E. Camara-Sola L. Salido-Díaz A. García-Alcántara D. E. Meleti B. Suberviola J. Riera L. Rellan M. Sanchez J. C. Robles E. Lopez R. Vicente E. Miñambres M. Santibañez M. Le Guen J. Moore N. Mason M. Windpassinger O. Plattner E. Mascha D. I. Sessler Outcomes Research U. Melia J. Fontanet J. P. van den Berg M. Baylor College of Medicine Neurology/Neurocritical Care Houston USA Erasmus Hospital Intensive Care Medicine Brussels Belgium Centre Hospitalier Régional Orléans Réaimation Médicale Orléans France University of British Columbia Vancouver Canada University of Adelaide Discipline of Acute Care Medicine Adelaide Australia Royal Adelaide Hospital Intensive Care Adelaide Australia Griffith University Menzies Health Institute of Queensland Gold Coast Australia University of Adelaide Discipline of Medicine Adelaide Australia Kingston General Hospital Clinical Evaluation Research Unit Kingston Canada University of Oxford Nuffield Department of Clinical Neurosciences Oxford UK Oxford University Hospitals NHS Foundation Trust Neurosciences Intensive Care Unit Oxford UK King’s College Hospital Department of Neurosurgery London UK King’ College London Institute of Psychiatry Psychology and Neuroscience Department of Clinical Neuroscience London UK Rice University Computer Science Houston USA Baylor College of Medicine Neurosurgery Houston USA Baylor College of Medicine Pediatric Cardiology Houston USA University of Twente Clinical Neurophysiology Enschede Netherlands Rijnstate Hospital Neurology Arnhem Netherlands Medical Spectrum Twente Clinical Neurophysiology Enschede Netherlands Medical Spectrum Twente Intensive Care Enschede Netherlands Rijnstate Hospital Intensive Care Arnhem Netherlands CHUV Department of Intensive Care Medicine Neuroscience Critical Care Research Group Lausanne Switzerland Aga Khan University Hospital Anaesthesiology Karachi Pakistan Centro Hospitalar São João Intensive Care Porto Portugal Faculty of Medicine University of Porto Medicine Porto Portugal Faculdade de Ciências Universidade do Porto Departamento de Matemática Porto Portugal Centro de Matemática Universidade do Porto Porto Portugal The Biomedical Research Networking Center in Bioengineering Biomaterials and Nanomedicine Zaragoza Spain Centro Hospitalar

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Designing enhanced classifiers using prior process knowledge: Regularized maximum-likelihood

Designing enhanced classifiers using prior process knowledge...

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2011 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS'11

作者： Esfahani, Mohammad Shahrokh Zollanvari, Amin Yoon, Byung-Jun Dougherty, Edward R. Department of Electrical and Computer Engineering Texas A and M University United States Childrens Hospital Informatics Program at Harvard-MIT Division of Health Sciences and Technology Harvard Medical School Brigham and Womens Hospital United States Phoenix AZ United States

ISBN: (纸本)9781467304900

We propose a novel optimization-based paradigm for designing enhanced classifiers. The proposed paradigm allows us to incorporate available prior process knowledge into classifier design, thereby improving the performance of the resulting classifiers. In this work, we focus on dynamical systems that can be represented as finite-state multi-dimensional stochastic processes that possess labeled steady-state distributions. Given prior operational knowledge of the process, our goal is to build a classifier that can accurately label future observations obtained from the steady-state, by utilizing both the available prior knowledge and the training data. Simulation results show that the proposed paradigm yields improved classifiers that outperform traditional classifiers that use only training data. ©2011 IEEE.

关键词： Random processes

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