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The increasing prevalence of multiprocessor and distributed systems in modern society is making it imperative to introduce the underlying principles of parallel/distributed computing to students at the undergraduate level. In order to meet the needs of our students for training in this critical area, the Computer Science Department at Southern Connecticut State University (SCSU) is currently in the process of implementing a curricular and laboratory development project that integrates key concepts and practical experiences in parallel computing throughout the undergraduate curriculum. The goal of this project is to build a strong foundation in parallel computing which would optionally culminate in advanced, senior-level specialized courses in parallel computing and/or senior research projects. This paper describes the laboratory facility we developed to support instruction in parallel and distributed computing and the parallel computing modules which were incorporated into three of our core undergraduate courses: data structures, operating systems, and programming languages. The laboratory facility enables us to provide our students with "hands-on" experiences in shared memory, distributed memory, and network parallelism. The modules and laboratory exercises give students the opportunity to experiment with a wide array of software and hardware environments and to gain a systematic exposure to the principles and techniques of parallel programming.
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In this paper we describe an information system that we have designed for students and researchers to conduct atmospheric studies using data that they have collected from multiple atmospheric instruments including two laser radar (lidar) systems. The lidar systems available for research include a monostatic Micro Pulse Lidar System and a bistatic imaging CLidar system. Complementary instruments for data analysis and ground truth specification include a nephelometer, sunphotometers and a weather station. Information structures within the system allow users to 1) label, describe and archive raw and derived datasets from multiple atmospheric instruments with associated metadata using NetCDF format, 2) link together coincident and co-located datasets from different instruments and 3) identify owner and verify user access rights of raw and derived datasets. Data analysis software tools have been developed in MATLAB to characterize and remove instrument artifacts based on experimental lidar studies, to analyze clear sky data to determine variability in atmospheric aerosol content over time and altitude, and to investigate cloud and aerosol patterns.
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A data management system has been developed for the Connecticut State University (CSU) Lidar Collaboratory to facilitate user authentication, scheduling of remote lidar instrumentation control sessions, storage and retrieval of lidar datasets and generation of new data products. In addition to providing for efficient archival and retrieval of lidar data products, a major design goal of the data management system is to support collaborative, multidisciplinary, atmospheric sciences research projects. In this paper, we describe the framework of the CSU Lidar Collaboratory data management system and how the system interacts with the data acquisition and data analysis software.
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Pattern recognition techniques for cloud type and cloud amount classification were applied to digital infrared SMS-1 data. The cloud classification results were used in a numerical radiation model to determine solar radiation during Phase III of the GARP Atlantic Tropical Experiment. In order to assess the effects on radiation computations of cloud information derived from both satellite and ship data, cloud analyses based on both data sources were prepared for input into the numerical radiation model. -from Authors
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