Networking Technology and Concepts. - Elahi, Ata (1997)
Keywords: dept.csc; faculty_collection; sabbatical_report

Analysis and classification of remote-sensed cloud imagery - DaPonte, John S.; Vitale, Joseph N.; Tselioudis, George; Rossow, William B. (Applications and Science of Artificial Neural Networks III, 1997)
The objective of this research is to automate the classification of clouds from satellite images providing a method for studying their properties over time. Analysis was applied to the International Satellite Cloud Climatology Project (ISCCP) low resolution (2.5 degrees per pixel) database for January 1987. Our approach differs from earlier studies by taking advantage of cloud top pressure and optical thickness from the ISCCP database, providing more accurate measures of cloud height with less dependency on the sun's angle of illumination. A total of 365 regions of interest (ROI), each classified Storm or Non Storm were used in the analysis. The algorithms used were Backpropagation Artificial Neural Network and Nearest Neighbor Pattern Classification. Each ROI was assigned on identification number between 1 and 365. One third of the ROIs were randomly selected for testing using a random number generator and the remaining ROIs were assigned to be training set. This process was repeated 29 times resulting in a mean classification error of 5.76% for the nearest neighbor algorithm and 3.97% for the backpropagation neural network.
Keywords: Backpropagation; Climatology; Clouds; Database systems; Image classification; Image processing; International Satellite Cloud Climatology Project; Multilayers; Neural networks; Random number generation; Remote sensing; Satellites; Testing; dept.csc

Comparison of genetic algorithm systems with neural network and statistical techniques for analysis of cloud structures in midlatitude storm systems - Parikh, Jo Ann; Daponte, J S; Vitale, Joseph N; Tselioudis, George (Pattern Recognition Letters, 1997)
Cloud analyses provide information which is vital to the detection, understanding and prediction of meteorological trends and environmental changes. This paper compares statistical, neural network and genetic algorithm methods for recognition and tracking of midlatitude storm clouds in sequences of low-resolution cloud-top pressure data sets. Regions of interest are identified and tracked from one image frame to the next consecutive frame in an eight-frame sequence. Classification techniques are used to determine the relationships between regions of interest in consecutive time frames. A genetic algorithm procedure is then used to revise classifier outputs to ensure that consistency constraints are not violated. © 1997 Elsevier Science B.V.
Keywords: Climate change; Cloud tracking; Clouds; Data sets; General circulation model; Genetic algorithm; Genetic algorithms; Midlatitude storm systems; Neural network; Neural networks; Pattern recognition; Storms; dept.csc

Stack smashing vulnerabilities in the UNIX operating system - Smith, Nathan P (1997)
Keywords: dept.csc; ⛔ No DOI found