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Ocean Color algorithm development environment for High-Speed Data Processing of GOCI-II 38

Ocean Color Algorithm Development Environment for High-Speed...

引用

38th IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Yang, Hyun Han, Hee-Jeong Heo, Jae-Moo Jeong, Jaehoon Lee, Taekyung Hu, Woong Kwak, Sunghee Korea Inst Ocean Sci & Technol Pusan South Korea Satree Initiat Co Ltd Ground Syst Div Daejeon South Korea

ISBN: (纸本)9781538671504

Geostationary Ocean Color Imager-II (GOCI-II), the follow-up satellite sensor platform of GOCI, will be launched in Mar. 2019. For estimating ocean color information from this new sensor data, the dedicated ocean color algorithms will be newly developed. However, the existing ocean color algorithm development methodology without consideration for the high-speed data processing will not be employed because the amount of GOCI-II data will be significantly large and their data will have to be processed and distributed in real time. In this study, therefore, we designed the ocean color algorithm development environment (OCADE) to support high-performance computing and storage using the parallelism. Also, OCADE enables the source code projects for GOCI-II ocean color products to be automatically built and deployed. We expect that GOCI-II ocean color algorithm developers will easily create and manage their parallelized source code projects at any time and any place through the OCADE cloud service. Additionally, they will be able to validate the output and evaluate the processing performance under OCADE in real-time. We believe that OCADE will contribute to the field of massive satellite data processing.

关键词： algorithm development environment GOCI-II ocean color

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Ocean Color algorithm development environment for High-Speed Data Processing of GOCI-II

Ocean Color Algorithm Development Environment for High-Speed...

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IEEE International Geoscience and Remote Sensing Symposium

作者： Hyun Yang Hee-Jeong Han Jae-Moo Heo Jaehoon Jeong Taekyung Lee Woong Hu Sunghee Kwak Korea Institute of Ocean Science and Technology Pusan Korea Ground Systems Division Satrec Initiative Co. ltd Daejeon Korea

关键词： algorithm development environment GOCI-II Ocean color

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Security in the Cloud: Understanding your Responsibility

Security in the Cloud: Understanding your Responsibility

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Conference on Cyber Sensing

作者： Bennett, Kelly W. Robertson, James US Army Res Lab Sensors & Electron Devices Directorate Adelphi MD 20783 USA Clearhaven Technol LLC Severna Pk MD 21146 USA

ISBN: (数字)9781510626881

ISBN: (纸本)9781510626881

The popularity of public cloud services continues to grow with Gartner predicting the total worldwide revenue to almost double from $145 billion in 2017 to $278 billion in 2021(1). Many cloud service types are components of this growth including Software-as-a-Service (SAAS), Platform-as-a-Service (PAAS) and Infrastructure-as-a-Service (IAAS). The use of cloud services brings many possible benefits such as scalability, high performance and availability, flexibility, cost effectiveness and security(2). However;each of these benefits comes with some responsibilities and requires a detailed knowledge of the specific cloud services used. For example, in Amazon Web Services (AWS) shared responsibility model for security, AWS is responsible for securing the facilities, physical security of hardware, network infrastructure, and the virtualization infrastructure. The cloud service customer is responsible for securing and managing the applications that run in the cloud, the operating systems, data-at-rest, data-in-transit, policies and other responsibilities. This paper works through several different use cases and provides the details for properly securing the services with which Army Research Laboratory (ARL) researchers interact. The use cases include sample configurations and descriptions required to fulfill the customer security responsibilities in a public cloud environment. Cloud services used include AWS Elastic Computer Cloud (EC2) Windows and Linux instances, Relational Database Services (RDS), Simple Cloud Storage Service (S3), Glacier S3 Storage, and DynamoDB. Challenges and approaches associated with delegating temporary security credentials, Identity and Access Management (IAM) service, and securing data-at-rest and data-in-transit will also be discussed.

关键词： Amazon Web Services Cloud computing Cloud-based security secure data environment shared security model AWS AODR MMSDB database SITCORE signature data processing sensor information testbed algorithm development environment

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Cloud-based Security Architecture supporting Army Research Laboratory's Collaborative Research environments 9

Cloud-based Security Architecture supporting Army Research L...

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9th Conference on Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR part of the SPIE Defense + Commercial Sensing Conference

作者： Bennett, Kelly W. Ward, Dennis W. Robertson, James US Army Res Lab Sensors & Electron Devices Directorate Adelphi MD 20783 USA Clearhaven Technol LLC Severna Pk MD 21146 USA

ISBN: (数字)9781510617827

ISBN: (纸本)9781510617827

Designing, operating and maintaining any system in today's complex Information Technology (IT) environments requires understanding and mitigating various levels of risk to the organization or to individuals associated with the operation of a system. How this risk is managed is critical to the organizations success. Multiple assets, technologies and skilled personnel are needed to select an appropriate set of security controls needed to protect individuals, operations and assets of the organization, and build or refine a security architecture based on best practices and risk management frameworks to adapt to ever changing threats. Developing secure applications and collaborating within a cloud-based environment adds both challenges and mitigations. Cloud environments are scalable and with the proper design and use can be secure and cost-effective. Special attention, processes and techniques associated with authentication and authorization require the formation and maintenance of users, groups, roles and policies. The U.S. Army Research Laboratory (ARL) provides America's Soldiers the technological edge through scientific research, technology development, and analysis. ARL provides scientific and technological innovation in a variety of technical disciplines, through direct in-house laboratory efforts and joint programs with government, industry, and academia. ARL's Open Campus is a collaborative endeavor, with the goal of building a science and technology ecosystem that will encourage groundbreaking advances in basic and applied research areas of relevance to the Army. Through the Open Campus framework, ARL scientists and engineers work collaboratively and side-by-side with visiting scientists in ARL's facilities, and as visiting researchers at collaborators' institutions. Through the relationships formed and the availability of secure, dynamic and scalable environments, rapid development, sharing and transition of technologies is possible. This technical paper propo

关键词： Amazon Web Services Cloud computing Cloud-based security secure data environment AWS AODR MMSDB database SITCORE signature data processing sensor information testbed algorithm development environment

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An Overview of the US Army Research Laboratory's Sensor Information Testbed Collaborative Research environment (SITCORE) and Automated Online Data Repository (AODR) Capabilities 8

An Overview of the US Army Research Laboratory's Sensor Info...

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8th Conference on Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR

作者： Ward, Dennis W. Bennett, Kelly W. US Army Res Lab ATTN RDRL SES S 2800 Powder Mill Rd Adelphi MD 20783 USA

ISBN: (数字)9781510608825

ISBN: (纸本)9781510608818;9781510608825

The Sensor Information Testbed COllaberative Research environment (SITCORE) and the Automated Online Data Repository (AODR) are significant enablers of the U.S. Army Research Laboratory (ARL)' s Open Campus Initiative and together create a highly-collaborative research laboratory and testbed environment focused on sensor data and information fusion. SITCORE creates a virtual research development environment allowing collaboration from other locations, including DoD, industry, academia, and collation facilities. SITCORE combined with AODR provides end-toend algorithm development, experimentation, demonstration, and validation. The AODR enterprise allows the U. S. Army Research Laboratory (ARL), as well as other government organizations, industry, and academia to store and disseminate multiple intelligence (Multi-INT) datasets collected at field exercises and demonstrations, and to facilitate research and development (R& D), and advancement of analytical tools and algorithms supporting the Intelligence, Surveillance, and Reconnaissance (ISR) community. The AODR provides a potential central repository for standardscompliant datasets to serve as the "go-to" location for lessons-learned and reference products. Many of the AODR datasets have associated ground truth and other metadata which provides a rich and robust data suite for researchers to develop, test, and refine their algorithms. Researchers download the test data to their own environments using a sophisticated web interface. The AODR allows researchers to request copies of stored datasets and for the government to process the requests and approvals in an automated fashion. Access to the AODR requires two-factor authentication in the form of a Common Access Card (CAC) or External Certificate Authority (ECA) certificate. Future versions of AODR will allow researchers to share both their results and algorithms with colleagues.

关键词： Amazon Web Services Cloud computing AODR MMSDB database SITCORE signature data HDF5 format sensor information testbed algorithm development environment

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A research environment for developing and testing object tracking algorithms

A research environment for developing and testing object tra...

引用

Conference on Visual Communications and Image Processing 2001

作者： Schoepflin, T Lau, C Garg, R Kim, DL Kim, YM Univ Washington Dept Bioengn Image Comp Syst Lab Seattle WA 98195 USA

ISBN: (纸本)0819439886

We present an integrated research environment (RAVEN) that we have developed for the purpose of developing and testing object tracking algorithms. As a Windows application, RAVEN provides a user interface for loading and viewing video sequences and interacting with the segmentation and object tracking algorithms, which are included at run time as plug-ins. The plug-ins interact with RAVEN via a programming interface, enabling algorithm developers to concentrate on their ideas rather than on the user interface. Over the past two years, RAVEN has greatly enhanced the productivity of our researchers, enabling them to create a variety of new algorithms and extend RAVEN's capabilities via plug-ins. Examples include several object tracking algorithms, a live-wire segmentation algorithm, a methodology for the evaluation of segmentation quality, and even a mediaprocessor implementation of an object tracker. After implementing an algorithm RAVEN makes it easy to present the results since it provides several mask display modes and output options for both image and video. We have found that RAVEN facilitates the entire research process, from prototyping an algorithm to visualization of the results to a mediaprocessor implementation.

关键词： algorithm development environment dynamic programming image segmentation interactive systems object tracking video segmentation

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