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40 results on '"Ramachandran, Rahul"'

16. Applying Deep Learning to Hail Detection: A Case Study.

Author

Pullman, Melinda, Gurung, Iksha, Maskey, Manil, Ramachandran, Rahul, and Christopher, Sundar A.

Subjects
DEEP learning, HAILSTORMS, METEOROLOGICAL observations, ATMOSPHERIC sciences, BRIGHTNESS temperature, MACHINE learning
Abstract

Deep learning is a subset of machine learning that uses deep neural networks (DNNs) capable of learning representations and extracting valuable information from vast data sets. Similarly, weather phenomena are often identified by patterns in data that serve as precursor signatures. Therefore, deep learning networks can be used to identify signatures of the weather phenomena, or possibly signatures not yet established by forecasters in addition to aiding forecasters in synthesizing the growing amount of meteorological observations. In this article, we demonstrate the value of deep learning for atmospheric science applications by providing a proof of concept, using deep learning for the detection of hail-bearing storms as a test case study. The deep learning network presented in this article obtains a higher precision when presented with multisource data and is able to identify a common feature associated with hail storms—decreased infrared brightness temperatures. This network and case study illustrate the capability of deep networks for the detection of weather phenomena and contribute to the growing awareness of deep learning among atmospheric scientists. [ABSTRACT FROM AUTHOR]

Published
2019
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36. Best security practices for android, blackberry, and iOS.

Author

Oh, Tae, Stackpole, Bill, Cummins, Emily, Gonzalez, Carlos, Ramachandran, Rahul, and Shinyoung Lim

Abstract

Mobile operating systems have expanded from running exclusively on smartphones to include tablets and music players as well, making them available to a larger audience. These operating systems are responsible for maintaining a multitude of schedules, messages, calls, music, documents, applications, photos, videos, and other personal data with the goal of increasing efficiency. Because of this, developers of mobile operating systems have built in configuration options to these operating systems to better protect the device from security vulnerabilities. However, not all of these operating systems have such settings turned on when the device is pulled out of the box. Furthermore, not all users are aware of how to best protect their information, nor the kind of issues that should be considered when owning and using these devices. This paper suggests best practices for the three major mobile platforms - Android, BlackBerry, and iOS - to mitigate and/or prevent unauthorized access, loss of information, and loss of confidentiality. [ABSTRACT FROM PUBLISHER]

Published
2012
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37. Introducing Provenance Capture into a Legacy Data System.

Author

Conover, Helen, Ramachandran, Rahul, Beaumont, Bruce, Kulkarni, Ajinkya, McEniry, Michael, Regner, Kathryn, and Graves, Sara

Subjects
*DATABASES, *XML (Extensible Markup Language), *QUALITY control, *EMPLOYEES' workload, *PROVENANCE (Geology)
Abstract

Accurate provenance information facilitates improved understanding of Earth science data and scientific reproducibility and can serve as an indicator of data quality. Provenance capture is an integral part of many modern workflow systems but may not have been considered in the design of legacy data production systems. Furthermore, in addition to data lineage, it is also important to capture contextual information needed for understanding how a data set was produced. This paper describes our experience in retrofitting a legacy data system to support capture, storage, and dissemination of provenance. Data inputs and transformations are logged automatically, while broader context information describing science algorithms and ancillary files is manually compiled. Provenance and context information are integrated for interactive user access and embedded into data files as XML documents compliant with the “Lineage” specification for geographic metadata defined by the International Organization for Standardization in the ISO 19115-2 standard. Lessons learned from this approach can inform others who need to incorporate provenance into a data system after the fact. [ABSTRACT FROM AUTHOR]

Published
2013
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38. Data Prospecting–A Step Towards Data Intensive Science.

Author

Ramachandran, Rahul, Rushing, John, Lin, Amy, Conover, Helen, Li, Xiang, Graves, Sara, Nair, U. S., Kuo, Kwo-Sen, and Smith, Deborah K.

Abstract

Data-intensive science is a scientific discovery process that is driven by knowledge extracted from large volumes of data rather than the traditional hypothesis driven discovery process. One of the key challenges in data-intensive science is development of enabling technologies to allow researchers to effectively utilize these large volumes of data in an effective manner. This paper introduces the concept of “data prospecting” to address the challenges of data intensive science. With data prospecting, we extend the familiar metaphor of data mining to describe an initial phase of data exploration used to determine promising areas for deeper analysis. Data prospecting enhances data selection through the use of interactive discovery engines. Interactive exploration enables a researcher to filter the data based on the “first look” analytics, discover interesting and previously unknown patterns to start new science investigations, verify the quality of the data, and corroborate whether patterns in the data match existing science theories or mental models. This paper describes our initial evaluation of the value of“data prospecting” to Earth Science researchers as part of their research process. The paper describes our discovery engine prototype to support data prospecting for specific data products along with its current limitations. Example science investigations from three different researchers using our prototype discovery engine to explore the Special Sensor Microwave/Imager and Sounder (SSM/I, SSMIS) data products are also presented. [ABSTRACT FROM PUBLISHER]

Published
2013
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39. A Proposed Earth Science Collaboratory for Remote Sensing Data Analysis.

Author

Kuo, Kwo-Sen, Lynnes, Christopher S., and Ramachandran, Rahul

Abstract

The past decade has seen many advancements in data management of remote sensing data, with much of the data available online. However, while the data access problem is largely (though not completely) solved, remote sensing data is still sometimes difficult to find, in part because of the myriad data sources. Data can also be difficult to use, especially for first-time users of a particular data set, due to complex data structures and data formats, as well as the need to understand how ancillary and quality information should be used in analysis. We propose the development of an Earth Science Collaboratory (ESC), which would be a rich science data analysis environment available to the community. The ESC would integrate tools and services with datasets and facilitate the sharing of the related knowledge. [ABSTRACT FROM PUBLISHER]

Published
2012
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40. SERVICE-ORIENTED ENVIRONMENTS FOR DYNAMICALLY INTERACTING WITH MESOSCALE WEATHER.

Author

Droegemeier, Kelvin K., Brewster, Keitch, Ming Xue, Weber, Daniel, Gannon, Dennis, Plale, Beth, Reed, Daniel, Ramakrishnan, Lavanya, Alameda, Jay, Wilhelmson, Robert, Baltzer, Tom, Domenico, Ben, Murray, Donald, Ramamurthy, Mohan, Wilson, Anne, Clark, Richard, Yalda, Sepideh, Graves, Sara, Ramachandran, Rahul, and Rushing, John

Subjects
GRID computing, WEATHER forecasting, COMPUTER systems, RESEARCH, WEATHER
Abstract

Describes a grid environment for research and education for the study of weather phenomena. Results of a research that is enabling a major shift toward dynamically adaptive responses to rapidly changing environmental conditions; Adaptive strategies in numerical weather prediction; Radar reflectivity forecasts from the Advanced Regional Prediction System model. INSET: Project Status..

Published
2005
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