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The backpropagation method is modified by replacing sigmoid function by sinusoidal function. The leaving law is also modified. The modified procedure shows great improvement over the original BP in terms of the number of neurons and the learning time. © 1992 IEEE.

New systolic architectures are proposed for the computation of the Fourier transform based on the generation of the coefficients of the transform during the computation. These architectures require less input/output pins on the chip. The new architectures are also extremely modular and cascadeable, thus, amenable for efficient VLSI implementation. VLSI complexity of the architectures are compared with the existing parallel architectures. © 1992 IEEE.

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.

This paper will discuss Internet based data collection and analysis utilizing a Windows 95 and a UNIX hosted system. Forms on the World Wide Web (WWW) that illustrate the use of this technology in medical research, conference registration, and. patient care will be highlighted. Some of the details involved with creating data collection forms will be presented. The paper concludes with the recommendation that the Health Telematics curricula should include a unit on creating Internet based data collection forms.

Temporal analysis has been applied to a sequence of cloud top pressure (CTP) images and cloud optical thickness (TAU) images stored in the International Satellite Cloud Climatology Project (ISCCP) D1 database located at the NASA Goddard Institute for Space Studies (GISS). Each pixel in the D1 data set has a resolution of 2.5 degrees or 280 kilometers. These images were collected in consecutive three-hour intervals for the entire month of April 1989. The primary objective of this project was to develop a sequence of storm tracks from the satellite images to follow the formation, progression and dissipation of storm systems over time. Composite images where created by projecting ahead in time and substituting the first available valid pixel for missing data and a variety of CTP and TAU cut-off values were used to identify regions of interest. Region correspondences were determined from one time frame to another yielding the coordinates of storm centers. These tracks were compared to storm tracks computed from sea level pressure data obtain from the National Meteorological Center (NMC) for the same time period. The location of sea level storm center provides insight as to whether storms have occurred anywhere in a region and can be helpful in determining the presence or absence of storms in a general geographic region.

The primary objective of this project is to define a methodology to depict the motion of deep convective cloud systems as observed form satellite imagery. These clouds are defined as clusters of pixels with Cloud Top Pressure (IPC) <EQ 440 millibars and Cloud Optical Thickness (TAU) >= 23 which are high in the atmosphere and sufficiently thick to produce significant rainfall. Clouds are one of the major factors in understanding the earth's climate. Evaluating cloud motion is important in understanding atmospheric dynamics and visualizations are vital because they provide a good way to observe change. IPC and TAU values have been collected for April of 1989 from the International Satellite Cloud Climatology Project, low resolution database for the northern latitudes between 30 and 60 degrees. Each of the 240 IPC and 240 TAU images consisted of 12 rows and 144 columns with each pixel representing a 280 km square on the globe collected in three-hour intervals. Individual images were color coded according to land, sea and clouds before being put into motion. Six animations have been produced which start with the original images, progress to include daily composite images and culminate with a collage. Animations of the original images have the advantage of relatively short intervals between still frames but have many undefined pixels, which are eliminated in the composites. The results of this project can serve as an example of how to improve the visualization of time varying image sequences.

The objective of this study is to compare statistical and unsupervised neural network techniques for determination of correspondences between storm system regions extracted from sequences of satellite images. Analysis was applied to the International Satellite Cloud Climatology Project (ISCCP) low resolution D1 database for selected storm systems during the period April 5 - 9, 1989. Cloud top pressure was used to delineate regions of interest and cloud optical thickness combined with spatial location was used to track regions throughout a given time sequence. The ability of the k-nearest neighbor classifier and of self-organizing maps to determine correspondences between storm regions was assessed. The two techniques generally yielded similar associations between regions of interest throughout the time sequence. Differences in final tracking results between the two techniques occurred primarily as a result of differences in the collections of points from a region in a time step t<SUB>2</SUB> that corresponded to a region in an earlier time step t<SUB>1</SUB>. The tracking results were also compared to the results obtained at the NASA Goddard Institute for Space Studies using sea level pressure data from the National Meteorological Center (NMC). For the storm systems investigated in this study, the storm tracks exhibited the same general tracking behavior with expected variations between cloud system storm centers and low sea level pressure centers.

A view of interactions in the undergraduate classroom is presented from several perspectives. Topics discussed include class perceptions of teacher as facilitator/authority/leader, grades versus performance appraisals, mixed-gender interactions, and subtle forms of cultural variations.

Accurate identification and tracking of synoptic-scale storm systems in the northern midlatitudes is important for understanding the structure and movement of the midlatitude cloud field which plays a major role in climate change. In this paper, a hybrid neural network/genetic algorithm (NN/GA) approach is presented that analyzes the behavior of storm systems from one time frame to the next. The goal of the hybrid neural network algorithm is to improve classifier output by reducing the number of infeasible solutions using constraint optimization techniques. The input to the hybrid neural network algorithm is the output from a traditional backpropagation neural network. The hybrid NN/GA analyzes the backpropagation neural network output for logical consistencies and makes changes to the classification results based on strength of neural network classifications and satisfaction of logical constraints. The results are compared with classification results obtained using linear discriminant analysis, k-nearest neighbor rule, and backpropagation neural network techniques.

An evolutionary system was developed for generation of complete tracks of northern midlatitude synoptic-scale storm systems based on optical flow and cloud motion analyses of global satellite-based datasets produced by the International Satellite Cloud Climatology Project (ISCCP). The tracking results were compared with low sea level pressure anomaly (SLPA) tracks obtained from the NASA Goddard Institute for Space Studies (GISS). The SLPA tracks were produced at GISS by analysis of meteorological, ground-based National Center for Environmental Prediction (NCEP) datasets. Results from the evolutionary system were also compared with results from using (a) the k-nearest neighbor rule (k-NN) and (b) self-organizing maps (SOM) to determine correspondences between consecutive locations within a track. The consistency of our evolutionary storm tracking results with the behavior of the low sea level pressure anomaly tracks, the ability of our evolutionary system to generate and evaluate complete tracks, and the close comparison between the results obtained by the evolutionary, k-NN, and SOM analyses of the ISCCP-derived datasets at tracking steps in which proximity or optical flow information sufficed to determine movement, demonstrate the applicability and the potential of evolutionary systems for tracking midlatitude storm systems through low-resolution ISCCP cloud product datasets.

This preliminary investigation considers undergraduate student perceptions with respect to their professional future. `No one warned me it would be like this,' and `These are the things that college never taught me,' are typical comments that are heard from the young workforce. This paper addresses future plans and predictions of students from two New England institutions of higher learning by utilizing a variety of strategies. Methods to elicit data include in-class activities and carefully designed questionnaires. These exercises have been designed to uncover images and themes concerning transition from college to the workplace. Issues include technical and communication skills, leadership roles, corporate politics, group dynamics, and gender diversity in the workplace.