[Book Publication of Computer Work Developed During My Academic Career] - Frew, Robert (Fall;Computer science., 1999)
Keywords: dept.csc; faculty_collection; sabbatical_report

Temporal Analysis of ISCCP Image Sequences. - DaPonte, John (1998)
Keywords: dept.csc; faculty_collection; sabbatical_report

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

The Development of an Image Classification Application. - Baradaran-Syed, Taranch (1996)
Keywords: dept.csc; faculty_collection; sabbatical_report

The Application of Chaotic Dynamics to Epidemiological and Biomedical Problems. - Vitale, Joseph (1995)
Keywords: dept.csc; faculty_collection; sabbatical_report

(Computer Vision) - Parikh, Josephine (1990)
Keywords: dept.csc; faculty_collection; sabbatical_report

Temporal Analysis of Meteorological Satellite Imagery: Sabbatical Leave Report for Spring, 1999 for the Computer Science Department, Southern Connecticut State University - Parikh, Jo Ann (Sabbatical Leave -- Southern Connecticut State University., 1999)
Keywords: Meteorology; Computer science; dept.csc; faculty_collection; sabbatical_report

How a Computer System Works - Brown, John A.; Workman, Robert S. (Arco Pub. Co., 1975)
Keywords: Electronic digital computers; dept.csc; faculty_collection

Fractal Feature Analysis and Classification in Medical Imaging - Chen, C.-C.; Daponte, J.S.; Fox, M.D. (IEEE Transactions on Medical Imaging, 1989)
Following Mandelbrot's fractal theory, it was found that the fractal dimension could be obtained in medical images by the concept of fractional Brownian motion. An estimation concept for determination of the fractal dimension based upon the concept of fractional Brownian motion was discussed. Two applications were found: 1) classification; 2) edge enhancement and detection. For the purpose of classification, a normalized fractional Brownian motion feature vector was defined from this estimation concept. It represented the normalized average absolute intensity difference of pixel pairs on a surface at different scales. The feature vector used relatively few data items to represent the statistical characteristics of the medical image surface and was invariant to linear intensity transformation. Finally, by calculating normalized fractional Brownian motion feature vectors in five different ultrasonic image surfaces, it was found that the classification of normal and abnormal ultrasonic liver images could be obtained from the differences between their feature vectors. For edge enhancement and detection application, a transformed image was obtained by calculating the fractal dimension of each pixel over the whole medical image. The fractal dimension value of each pixel was obtained by calculating the fractal dimension of a 7 x 7 pixel block centered on this pixel. Preliminary results using projection radiographs suggest that the fractal based image transformation appears to hold promise as an edge enhancement and preprocessing algorithm that does not increase noise in the way that gradient operators do. © 1989 IEEE
Keywords: Acoustic Imaging; Fractal Feature Analysis; Fractional Brownian Motion; Image Processing--Enhancement; Imaging Techniques; Mathematical Techniques--Vectors; Medical Imaging Classification; Probability--Random Processes; Ultrasonic Liver Images Classification; Ultrasonics--Medical Applications; dept.csc

Statistical classification of ultrasonic image texture - DaPonte, John S.; Gelber, Joel; Fox, Martin D. (Publ by IEEE, 1989)
Qualitative analysis is important because it is not subjective and does not have the potential for variation from one observer to another. A description is given of how statistical hypothesis testing can be used to select the quantitative descriptors best capable of distinguishing between normal and abnormal liver texture. Information is also presented on how both parametric and nonparametric discriminant analysis can be applied to determine how well the quantitative analysis compares with the qualitative diagnosis supplied for each case studied.
Keywords: Acoustic Imaging--Digital Techniques; Biomedical Engineering; Liver Disease Diagnosis; Quantitative Descriptor Selection; Statistical Classification Techniques; Statistical Hypothesis Testing; Statistical Methods--Statistical Tests; Ultrasonic Devices--Medical Applications; Ultrasonic Image Texture Classification; dept.csc; ⛔ No DOI found

Effect of cooccurrence displacement vector on quantitating ultrasonic image texture - DaPonte, John S.; Gelber, Joel; Fox, Martin D. (Publ by IEEE, 1988)
While cooccurrence matrices have been shown to be helpful in quantitating image texture, the amount of data associated with them can rapidly become unmanageable because a separate cooccurrence matrix can be calculated for each displacement vector chosen. Here, a method for choosing the direction of the displacement vector that is based on the most dominant edge obtained from gradient analysis is discussed. Also, the anatomy of the liver is used to suggest the most important intersample spacing in constructing cooccurrence matrices for the evaluation of diffuse liver disease.
Keywords: Acoustic Imaging; Cooccurrence Displacement Vector; Cooccurrence Matrices; Diffuse Liver Disease; Gradient Analysis; Image Processing; Mathematical Techniques--Vectors; Ultrasonic Image Texture; Ultrasonics; dept.csc; ⛔ No DOI found

Fast Hartley Transforms for Spectral Analysis of Ultrasound Doppler Signals - Paik, C.H.; Cote, G.L.; DaPonte, J.S.; Fox, M.D. (IEEE Transactions on Biomedical Engineering, 1988)
Spectral analysis of Doppler ultrasound has been known to yield valuable information to assess the state of circulation in the peripheral blood vessels. In the past, the raw Doppler data have been directly input into a dedicated spectrum analyzer or, more recently, transformed on a microcomputer with the fast Fourier technique. The fast Hartley technique is used to transform these data. The Hartley transform has the advantages of being a purely real-numbered transform, and therefore for real Doppler data, is not only more conceptually straightforward, but also requires less computer memory, is simpler to calculate, and is better suited to large-scale integration implementation. © 1988 IEEE
Keywords: Algorithms; BIOMEDICAL ENGINEERING; COMPUTERS, MICROCOMPUTER; Carotid Arteries; Comparative Study; FAST HARTLEY TRANSFORMS; Human; MATHEMATICAL TRANSFORMATIONS; Mathematical Computing; Numerical Analysis, Computer-Assisted; SIGNAL PROCESSING -- Digital Techniques; SPECTRUM ANALYSIS; Support, Non-U.S. Gov't; Support, U.S. Gov't, P.H.S.; Time Factors; ULTRASOUND DOPPLER SIGNALS; Ultrasonics; VELOCIMETERS -- Ultrasonic Doppler; algorithm; blood flow; computer analysis; dept.csc; doppler flowmeter; methodology

Gray Level Histogram Analysis of Ultrasonic Images on a Microcomputer Based System. - DaPonte, John S.; Fox, Martin D. (IEEE, 1987)
Recent developments in image digitization have made possible a more quantitative analysis of ultrasonic imagery of the liver, which could lead to a more sensitive method for changes in liver texture as an aid in the diagnosis of liver disease. The approach described is the statistical analysis of one-dimensional intensity (gray-level) histograms obtained from B-mode ultrasonic images. First-order statistical parameters are used to characterize the location, variability, skewness and kurtosis of the histograms. One typical normal study and one typical abnormal study are presented to shown the type of results that have been obtained.
Keywords: ACOUSTIC IMAGING - Medical Applications; B-MODE ULTRASONIC IMAGES; BIOMEDICAL ENGINEERING - Imaging Techniques; GRAY LEVEL HISTOGRAM ANALYSIS; IMAGE PROCESSING; LIVER IMAGING; ULTRASONICS - Imaging Techniques; dept.csc; ⛔ No DOI found

Nonlinear Analysis of a New Sine Wave Oscillator. - Elahi, Ataollah; Guyton, Robert D. (IEEE, 1987)
A new type of RC op oscillator has been designed. For amplitude stabilization, diodes are added in the feedback of the linear circuit. A model has been developed for a nonlinear element, which affects the frequency of oscillation. The model can be used to design the oscillator for different frequencies and to calculate frequency and amplitude sensitivity with respect to the parameter of the system.
Keywords: NONLINEAR ANALYSIS; OSCILLATORS; RADIO EQUIPMENT; SINE WAVE OSCILLATOR; dept.csc

Personal Computer Applications Course. - Workman, Robert (ACM, 1984)
A personal computer applications course has been developed. This course is a follow up to an introductory programming course for non-computer science majors. The primary objective of the course is to introduce the major personal computer applications areas: operating system use, word processing, spreadsheet programming, data base management, and communications. For each area, there will be a discussion of its use and related problems. Students will use a representative and a comparison will be made with other systems. The course will be taught using Apple IIe's or Commodore 64 computers. A course outline has been created and approved. The course will be offered for the first time in the Spring of 1984. Budget considerations, the practical difficulties involved with students using copyrighted software, and a desire to have students leave with software they can take with them, make it attractive to use public domain software when possible. Current research is directed towards finding and documenting public domain software for use in this course. Principal sources being investigated are the program libraries of personal computer users groups and educational cooperatives.
Keywords: ABSTRACT ONLY; COMPUTER PROGRAMMING; COMPUTERS, MICROCOMPUTER - Applications; PERSONAL COMPUTER APPLICATIONS COURSE; PUBLIC DOMAIN SOFTWARE; dept.csc

Satellite cloud analyses for a radiation model. - Parikh, J.A.; Ball, J.T. (Pattern recognition in practice. Proc. workshop Amsterdam, May 1980, 1980)
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
Keywords: Atlantic Tropical Experiment; dept.csc; numerical radiation model; pattern recognition techniques; ⛔ No DOI found

CyberEthics - Fodor, J.L. (Association for Computing Machinery, Inc, 1994)
Keywords: dept.csc

Effect of sinusoidal function on backpropagation learning - Karimi, B.; Ashenayi, K.; Seyed, T.B. (Association for Computing Machinery, 1992)
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.
Keywords: Backpropagation; Backpropagation learning; Circuit simulation; Learning time; Sigmoid function; Sinusoidal functions; Software engineering; dept.csc

Systolic architectures for parallel Fourier transform - Baradaran-Seyed, T.; Johnson, L.G.; Karimi, B. (Institute of Electrical and Electronics Engineers Inc., 1991)
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.
Keywords: Input/output; Parallel architectures; Systolic architecture; VLSI circuits; VLSI implementation; dept.csc

Experiences in parallel/distributed computing for undergraduate computer science majors - Parikh, J.A. (1996)
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.
Keywords: Computer operating systems; Computer science; Computer software; Curricula; Distributed computing; Engineering education; Node configurations; Parallel processing systems; Parallel programming; Professional aspects; Project management; Social aspects; Software configurations; Students; Teaching; dept.csc

Internet based data collection and analysis - Workman, R.; Beatty, E.; Workman, D. (IOS Press, 1998)
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.
Keywords: *Computer Communication Networks; *Data Collection/mt [Methods]; Computer Communication Networks; Data Collection; Data Collection/is [Instrumentation]; Data acquisition; Data collection; Health; Humans; Internet based; Medical Informatics; Medical Informatics/og [Organization & Administration]; Medical research; Patient care; Telematics; Wireless telecommunication systems; World wide webs (WWW); article; computer network; dept.csc; human; information processing; instrumentation; medical informatics; methodology; organization and management; ⛔ No DOI found

Cognitive Predictors of Success in Computer Programmer Training - Jacobs, Stuart J. (ACM, 1973)
The aptitudes and abilities required for the position of programmer, within the computer industry, have yet to be fully studied and their inter-relationships known. Although the industry is relatively new, a substantial amount of research in the areas of personnel selection, evaluation and job requirements has been undertaken. Yet these studies have confined themselves primarily to the use of interest scales, aptitude and achievement tests as overall predictors for on-the-job success rather than in the study of the cognitive factors pertinent to the tasks of which programming is composed. In a study by Deutsch and Shea, Inc. (1963), the relationship between the programmer and the computer is seen as analogous to that of the mahout and his elephant. As with the mahout, the programmer uses his intelligence, skills and abilities in the control and guidance of a powerful and flexible, yet non-intelligent, tool in the performance of specific finite operations which contribute to the completion of more complex tasks. It is the programmer who, when presented with a problem from science, engineering or business, must work out a solution. John and Miller (1957) state that all problems have two general parts: the specific components involved (i.e., data, etc.) and the relationships which are the orderings of or changes to the specific components.
Keywords: dept.csc

A survey of space allocation algorithms in use in architectural design in the past twenty years - Frew, Robert Simpson, Ph.D. (ACM, 1980)
The paper presents methods of space allocation applicable to architectural design. These techniques have been developed in the past twenty years and are presented in this paper in such a way that they mav also be applied to other disciplines. Four categories are presented that identify the variations in the dimensioning of the elements, either unit dimension or variable dimension, and the variation in the shape of the boundary, either a simple rectangle or a multi-faceted boundary.
Keywords: Architectural design; Computer aided design; Space allocation; Space allocation algorithm; Unit dimension; Variable-dimension; dept.csc

Enhancement of chest radiographs with gradient operators - DaPonte, J.S.; Fox, M.D. (IEEE Transactions on Medical Imaging, 1988)
The use of gradient operators for image enhancement has been widely reported in the literature, but they have not been used routinely in the medical arena, particularly in the most common radiographic plain film procedure, chest radiographs. Gradient operators such as Sobel and Roberts operators, not only enhance image edges but also tend to enhance noise. Overall, the Sobel operator was found to be superior to the Roberts operator in edge enhancement. A theoretical explanation for the superior performance of the Sobel operator was developed based on the concept of analyzing the x and y Sobel masks as linear Alters. By applying pill box, Gaussian, or median filtering prior to applying a gradient operator, noise was reduced, but the pill box and Gaussian filters were much more computationally efficient than the median filter with approximately equal effectiveness in noise reduction. © 1988 IEEE
Keywords: ENHANCING CHEST RADIOGRAPHS; GRADIENT OPERATORS; IMAGE EDGES ENHANCEMENT; IMAGE PROCESSING - Enhancement; RADIOGRAPHY; RADIOGRAPHY - Medical Applications; dept.csc

Automatic techniques for extraction of geological fracture patterns**the work reported in this paper was performed under a nasa-asee summer faculty fellowship program at nasa/gsfc. - Parikh, Jo Ann (Pattern Recognition in Practice, 1986)
Keywords: dept.csc

Temporal analysis of remotely sensed image sequences - DaPonte, John S.; Vitale, Joseph N.; Parikh, Jo Ann; Tselioudis, George (Society of Photo-Optical Instrumentation Engineers, 1999)
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.
Keywords: Climatology; Cloud optical thickness; Cloud top pressure (CTP) images; Clouds; Communication satellites; Remote sensing; Storms; dept.csc

Visualization of deep convective image sequences - DaPonte, John S.; Parikh, Jo Ann (Visual Information Processing VIII, 1999)
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.
Keywords: Animation; Atmospheric turbulence; Cloud motion dynamics; Cloud optical thickness; Cloud top pressure; Clouds; Database systems; Deep convective cloud systems; Image segmentation; Imaging systems; International satellite cloud climatology; Motion compensation; Satellite imagery; Time varying image sequences; Visualization; Weather satellites; dept.csc

Unsupervised classification techniques for determination of storm region correspondences - Parikh, Jo Ann; DaPonte, John S.; Vitale, Joseph N. (Applications and Science of Computational Intelligence II, 1999)
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 t2 that corresponded to a region in an earlier time step t1. 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.
Keywords: Algorithms; Climate change; Climatology; Feature extraction; Image processing; Mapping; Nearest neighbor classifier; Neural networks; Self organizing map; Storm tracking; Storms; dept.csc; ⛔ No DOI found

Building the bridge from classroom to industry - Lancor, Lisa; Karanian, B A (Frontiers in Education Conference, 1999)
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.
Keywords: Classroom strategies; Computer aided instruction; Engineering education; Interactive computer systems; Interactive discussions; Professional aspects; Strategic planning; Students; dept.csc; ⛔ No DOI found

Hybrid neural network techniques for storm system identification and tracking - Parikh, Jo Ann; Daponte, John S; Vitale, Joseph N (IEEE, 1999)
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.
Keywords: Backpropagation; General circulation models; Genetic algorithms; Hybrid neural networks; Meteorology; Neural networks; Optimization; Storms; Tracking (position); dept.csc; ⛔ No DOI found

An evolutionary system for recognition and tracking of synoptic-scale storm systems - Parikh, Jo Ann; Daponte, John S; Vitale, Joseph N; Tselioudis, George (Pattern Recognition Letters, 1999)
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.
Keywords: Computational methods; Data structures; Image analysis; Nearest neighbor analysis; Optical flows; Pattern recognition systems; Self-organizing maps; Storms; Synoptic-scale storm systems; Weather forecasting; cloud; dept.csc; pattern recognition; satellite imagery; storm track; synoptic meteorology

College to work transitions: students draw their futures - Lancor, Lisa; Karanian, B A (1998)
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.
Keywords: Employment; Engineering education; Professional aspects; Student perceptions; Students; dept.csc

Application of evolutionary techniques to temporal classification of cloud systems using satellite imagery - Parikh, Jo Ann; Daponte, John S; Vitale, Joseph N; Tselioudis, George (SPIE, 1998)
The objective of this research is to automate the classification of the temporal behavior of storm cloud systems based on measurements derived from consecutive satellite images. The motivation behind this study is to develop improved descriptions of cloud dynamics which can be used in general circulation models for prediction of global climate change. Analysis was applied to the International Satellite Cloud Climatology Project (ISCCP) low resolution cloud top pressure database for the first six days in April, 1989. A total of 296 midlatitude storm cloud components were tracked between consecutive 3-hour time frames. For each pair of components, temporal correspondence events were classified as either 1.) direct, 2.) merge, 3.) split, or 4.) reject. The reject class, which was used primarily to categorize pairs of unrelated systems, included storm cloud system dissipation and creation as well. Statistical, neural network, and evolutionary techniques were developed for finding solutions to the storm cloud correspondence problem. Evolutionary techniques applied to the problem consisted of 1.) a constraint-handling hybrid evolutionary technique and 2.) a genetic local search algorithm. The results demonstrate the potential of evolutionary techniques to yield meteorologically-feasible solutions, given appropriate constraints, to the two-frame storm tracking problem. © 1998 SPIE. All rights reserved.
Keywords: Artificial intelligence; Climate change; Climate models; Cloud tracking; Correspondence problem; Correspondence problems; Evolutionary techniques; General circulation model; Genetic algorithms; Genetic local search algorithm; Global climate changes; Image classification; International satellite cloud climatology projects; Neural networks; Satellite imagery; Storms; Temporal classification; dept.csc

Developing integrated Web and database applications using JAVA applets and JDBC drivers - Yang, Andrew; Linn, James; Quadrato, David (ACM, 1998)
In this paper we have discussed different types of JDBC drivers under the context of a two-tier client/server model. However, it is entirely possible to use them to develop a multi-tier client/server application. The integration of web servers with database servers via the use of JAVA applets and JDBC drivers is useful for the teaching of database programming and web-based application development. The applet that we have developed, along with our experience of configuring the JDBC and JAVA environment, was used in a database course. Students built more complicated database/web applications on top of this sample applet. Future extension of our work may involve the following items: • the security implication of using JDBC drivers in a multiple, heterogeneous DBMS environment • the possible interaction of JDBC with firewalls and proxy servers • the evaluation of JDBC drivers under the context of real-world applications, especially their reliability and performance.
Keywords: Computer science; Database systems; High level languages; Java database connectivity (JDBC) drivers; Java programming language; Object oriented programming; Wide area networks; dept.csc

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

Texture analysis of ultrasonic images using backpropagation neural networks - Parikh, Jo Ann; Daponte, John S; Damodaran, Meledath (Springer Verlag, 1995)
Backpropagation neural networks are applied to the problem of characterization of ultrasonic image texture to detect abnormalities in tissue texture which are indicative of liver disease. Twenty-one texture features were extracted from regions of interest in digitized ultrasonic images. A feature subset, identified by a stepwise selection process, formed the sample input to the networks together with the physician-supplied diagnosis. The classification performance of the backpropagation network is evaluated using a jackknife testing procedure. The performance of the networks is compared with results obtained from linear discriminant analysis and logistic regression techniques. © Springer-Verlag Berlin Heidelberg 1995.
Keywords: Back propagation neural networks; Backpropagation; Backpropagation network; Classification performance; Computer graphics; Diagnosis; Discriminant analysis; Image analysis; Image texture; Linear discriminant analysis; Logistic regressions; Neural networks; Regions of interest; Testing procedure; Texture analysis; Ultrasonic applications; Ultrasonic imaging; dept.csc

Pancreatic cancer among workers processing synthetic resins - Selenskas, Sandra; Teta, M Jane; Vitale, Joseph N (American Journal of Industrial Medicine, 1995)
A nested case‐control study was conducted to investigate whether an excess of pancreatic cancer, identified in a cohort mortality study with follow‐up from 1946 through 1988. was associated with potential workplace exposures at a New Jersey plastics manufacturing and research and development facility. The study population included 28 male pancreatic cancer cases and 140 randomly selected controls, matched on year of birth and at risk (alive) at the time of the case death. Using plant work history records, department assignments for the two groups were compared according to duration and time since first assignment. Workers assigned to a work area that processed vinyl resins and polyethylene (PE) were shown to be at increased risk. Men assigned more than 16 years to this department had a significantly increased risk ratio of 7.15 (95% confidence intervals [CI]: 1.28–40.1). No excess was seen with shorter duration assignments. Seven of the nine cases began working in this area in the 1940s. Average latency was 32 years, and all but three cases worked 20 years or more in this unit. Over the study period, significant exposure‐related process changes occurred, in addition to the use of numerous chemical additives. Although vinyl and PE processing operations could not be analyzed separately, the pancreatic cancer excess is more likely to be related to vinyl processing. Identification of a causative agent or combination of agents would require investigations with more detailed exposure information. Copyright © 1995 Wiley Periodicals, Inc., A Wiley Company
Keywords: Aged; Aged, 80 and over; Case-Control Studies; Cohort Studies; Human; Male; Middle Age; Occupational Diseases; Pancreatic Neoplasms; Polyethylenes; Risk Factors; Time Factors; Vinyl Compounds; adult; aged; article; cancer mortality; cancer risk; case control study; case‐control study; controlled study; dept.csc; diethylhexyl phthalate; female; human; industry; major clinical study; male; occupational cancer; occupational exposure; occupational hazard; pancreas cancer; pancreatic neoplasm; plasticizer; plastics; polyethylene derivative; polyvinyl chloride; vinyl derivative

Ultrasonic image texture classification using Markov random field models - DaPonte, John S.; Parikh, Jo Ann; Vitale, Joseph N.; Decker, James (Applications of Artificial Neural Networks V, 1994)
Over the past several years we have been interested in the supervised classification of ultrasonic images of the liver based on quantitative texture features. Our most recent efforts are concerned with the inclusion of features computed from Markov random fields. After adding four such features to our existing model containing 17 features, we employed stepwise discriminant analysis to identify the features that could best discriminate among 184 previously classified normal and abnormal ultrasonic images. Three of the four features derived from Markov random field models were identified by stepwise discriminant analysis as being good discrimination along with 6 existing features. From these results we constructed a backpropagation neural network with an input layer consisting of 9 nodes. We found that this new model yielded slightly better results when compared to earlier models. Our most recent results yielded a sensitivity of 81%, a specificity of 77% and an overall accuracy of 79%.
Keywords: Back propagation neural networks; Discriminant analysis; Good discriminators; Image classification; Image segmentation; Image texture; Image texture classification; Markov Random Field model; Markov Random Fields; Markov processes; Multilayer neural networks; Overall accuracies; Stepwise discriminant analysis; Supervised classification; Textures; Ultrasonic imaging; dept.csc

Hybrid systems for constraint-based spatial reasoning - Parikh, Jo Ann (Machine Intelligence and Pattern Recognition, 1994)
Constraint-based spatial reasoning problems frequently arise in the area of military mission planning. In this domain, mission planners employ complex criteria, which may include numeric and optimization constraints in addition to logical constraints and rules, to develop engineering construction and resource deployment plans. Automated planning aid systems for the military must have the capability to represent the various types of constraints used in human decision-making and must be able to provide efficient and optimal or near optimal solutions to the resulting constraint satisfaction problems. This paper describes a methodology for transforming constraint satisfaction problems into nonlinear optimization problems and for solving the resulting optimization problems using a hybrid neural network/genetic algorithm procedure. The method is applied to illustrative problems which employ different types of constraints for determination of future construction sites. The results of the experiments demonstrate the potential of this methodology for finding feasible and optimal solutions to nonlinear optimization problems. © 1994, Elsevier B.V.
Keywords: dept.csc

Evaluation of the fractal dimension as a pattern recognition feature using neural networks - Daponte, John S; Parikh, Jo Ann; Decker, James; Vitale, Joseph N (SPIE, 1993)
In the past fractal dimension has often been computed using a stochastic approach based on a random walk process, which has been found to be very time consuming. More recently, mathematical morphology has been used to compute the fractal dimension in a more timely fashion. This paper describes how the fractal dimension computed using mathematical morphology can be used in the texture analysis of ultrasonic imagery. The discriminatory ability of the fractal dimension as a pattern recognition feature is evaluated and compared to more traditional parameters. This analysis includes comparisons with statistical features in which each parameter is treated as an independent variable and in which interactions between those variables are evaluated. Pattern recognition techniques include Stepwise Discriminant Analysis, Linear Discriminant Analysis, and Nearest Neighbor Analyisis in addition to Backpropagation Neural Network Classifiers. Our results identify the fractal dimension as one of the most important parameters for distinguishing between normal and abnormal livers. In this study, consisting of 186 images, a significant statistical difference was found for both the mean and standard deviation of the fractal dimension between the normal and abnormal groups using parametric and nonparametric statistical techniques. © 1993 SPIE. All rights reserved.
Keywords: Back propagation neural networks; Backpropagation; Discriminant analysis; Fractal dimension; Independent variables; Linear discriminant analysis; Mathematical morphology; Mean and standard deviations; Morphology; Neural networks; Pattern recognition; Pattern recognition techniques; Statistical differences; Statistical techniques; Stepwise discriminant analysis; Stochastic systems; Textures; dept.csc

Neural network solutions to logic programs with geometric constraints - Parikh, Jo Ann; Werkheiser, Anne; Subrahmanian, V S (SPIE, 1993)
Hybrid knowledge bases (HKBs), proposed by Nerode and Subrahmanian, provide a uniform theoretical framework for dealing with the mixed data types and multiple reasoning modes required for solving logical deployment problems. Algorithms based on mixed integer linear programming techniques have been developed for the syntactic subset of HKBs corresponding to function-free Prolog-like logic programs. In this study, we examine the ability of neural networks to solve a more comprehensive set of problems expressed within the hybrid knowledge base framework. The objective of this research is to design and implement a nonlinear optimization procedure for solving extended logic programs with neural networks. We focus upon two types of extensions which are typically required in the formulation of logical deployment problems. The first type of extension, which we shall refer to as a Type I extension, consists of embedding numerical and geometric constraints into logic programs. The second type of extension, which we shall call a Type II extension, consists of incorporating optimization problems into logic clauses. © 1993 SPIE. All rights reserved.
Keywords: Computer circuits; Deployment problems; Design and implements; Geometric constraint; Integer programming; Knowledge based systems; Logic programming; Mixed-integer linear programming techniques; Network solutions; Neural networks; Non-linear optimization; Nonlinear programming; Optimization problems; PROLOG (programming language); Theoretical framework; dept.csc

Selective detection of linear features in geological remote sensing data - Parikh, Jo Ann; DaPonte, John S.; DiNicola, Emily G.; Pedersen, Robert A. (SPIE, 1992)
One of the major problems in the development of computer- A ssisted systems for geologic mapping is how to individualize the system to meet user needs. Ideally, the system should be responsive to specifications of desired types of output structures. Also the system should be able to incorporate the user's knowledge of regional characteristics into the feature extraction/selection and classification components. Automatic techniques for classification of remote sensing data typically require relatively large, labeled training sets which are well-organized with respect to the desired mapping between input and output patterns. The present paper focuses on the feature extraction/selection component of the system. Kohonen self-organizing feature maps in conjunction with image processing procedures for linear feature extraction are used for explorative data analysis, feature selection, and construction of exemplar patterns. The results of training Kohonen feature maps with different pattern sets and different feature combinations provide insight into the nature of pattern relationships which enables the user to develop sets of positive and negative training patterns for the classification component. © 1992 SPIE. All rights reserved.
Keywords: Automatic technique; Classification (of information); Conformal mapping; Data mining; Explorative data analysis; Extraction; Feature extraction; Feature extraction/selection; Image processing; Kohonen self-organizing feature map; Linear feature extraction; Regional characteristics; Remote sensing; Remote sensing data; Selective detection; Self organizing maps; dept.csc

Detection of liver metastisis using the backpropagation algorithm and linear discriminant analysis - Daponte, John S; Parikh, Jo Ann; Katz, David A (SPIE, 1991)
The purpose of this study was to compare the classification capabilities of the backpropagation algorithm and linear discriminant analysis for detecting liver metastisis using image texture features obtained from ultrasonic images of the liver. Twenty-one quantitative parameters were obtained from 134 regions of interest of equal size. The images were collected by the same radiologist on the same imager with the controls adjusted for variations in patient body size so as to produce images of consistent quality. Quantitative features were divided so that 13 were first-order statistics, 6 were second-order statistics, and 2 were image gradient parameters. The same features were processed by both the backpropagation algorithm and linear discriminant analysis using `jack-knife'' testing and the results of each computer- generated classification was compared to the supplied diagnosis in an effort to determine which method could best identify patterns. For this particular application, the backpropagation neural network was found to have slightly superior classification results (87) than linear discriminant analysis (83).
Keywords: Backpropagation; Biomedical Engineering; Linear Discriminant Analysis; Medical Imaging; Neural Networks - Medical Applications; Pattern Classification; Pattern Recognition - Medical Applications; dept.csc

Classification of ultrasonic image texture by statistical discriminant analysis and neural networks - Daponte, John S; Sherman, Porter (Computerized Medical Imaging and Graphics, 1991)
In this paper the ability of two common statistical discriminant analysis procedures are compared with two commercial neural network software packages. The major objective of this study was to determine which of the procedures could best discriminate between normal and abnormal ultrasonic liver textures. The same set of features were input into both statistical discriminant analysis procedures and both neural network models. Preliminary results have found the restricted Coulomb Energy (RCE) neural network model to have a testing accuracy of 90.6% which is approximately 10% better than any of the other techniques investigated. © 1991.
Keywords: Algorithms; Artificial Intelligence; Computer Programming - Algorithms; Discriminant Analysis; Human; Image Processing, Computer-Assisted; Image Texture; Image texture; Imaging Techniques; Linear Discriminant Analysis; Linear discriminant analysis; Liver; Liver Diseases; Models, Biological; Nearest Neighbor Analysis; Nearest neighbor analysis; Neural Networks - Medical Applications; Neural networks; Pattern Recognition; Reference Values; Software; Support, Non-U.S. Gov't; Ultrasonic Applications - Medical Applications; Ultrasonic Image Texture; Ultrasound; article; artificial intelligence; dept.csc; echography; image analysis; priority journal; tissue characterization

Comparison of backpropagation neural networks and statistical techniques for analysis of geological features in Landsat imagery - Parikh, Jo Ann; Daponte, John S; Damodaran, Meledath; Karageorgiou, Angelos; Podaras, Petros (Publ by Int Soc for Optical Engineering, 1991)
Backpropagation neural networks have been developed for detection of geological lineaments in the Landsat Thematic Mapper (TM) imagery of the Canadian Shield using edge images as input and digitized lineament maps as the desired output. Lineament detection is a challenging problem for traditional image processing and pattern recognition techniques. Many linear features observable in geological image data do not represent lineaments, and the presence and extent of lineaments must be inferred from contextual information. In order to compare the ability of neural networks and conventional classifiers to recognize lineaments prior to performing edge/line element grouping operations, various gradient and curvature features are extracted from the image data set. Selected features from this group formed the inputs to backpropagation neural networks, linear discriminant classifiers, and nearest-neighbor classifiers. The neural network results were compared with the results obtained using conventional classifiers for sample training and test sets. The trained neural network was then applied to the edge image to mask out those edge points which had been classified as non- lineament points.
Keywords: Artificial Neural Networks; Backpropagation; Feature Extraction; Geological Features; Geological Surveys; Image Processing - Image Analysis; Landsat Imagery; Learning Systems; Neural Networks - Applications; Pattern Recognition; Statistical Analysis; dept.csc

Application of neural networks to pattern recognition problems in remote sensing and medical imagery - Parikh, Jo Ann; DaPonte, John S.; Damodaran, Meledath; Sherman, Porter (Applications of Artificial Neural Networks, 1990)
The primary objective of this research is the development and testing of neural network models for two fundamental computer vision tasks: edge/line detection and texture analysis. In order to test the ability of the neural network models to detect patterns in images we used both remote sensing data and medical imagery. Neural network models for edge and line detection were used to detect geological lineaments in Landsat data. Neural network models for the analysis of image texture variations were used on ultrasonic images to distinguish patients with normal liver scans from patients with diffuse liver disease. 1.
Keywords: Artificial Intelligence--Applications; Artificial Neural Networks; Computer Vision; Edge/Line Detection; Imaging Techniques--Medical Applications; Medical Imagery; Neural Networks; Pattern Recognition; Pattern Recognition Systems; Remote Sensing; Texture Analysis; dept.csc