Parallel Genetic Algorithms for the DAG Vertex Splitting Problem

Directed Acyclic Graphs are often used to model circuits and networks. The path length in such Directed Acyclic Graphs represents circuit or network delays. In the vertex splitting problem, the objective is to determine a minimum number of vertices from the graph to split such that the resulting graph has no path of length greater than a given δ. The problem has been proven to be NP-hard. A Sequential Genetic Algorithm has been developed to solve the DAG Vertex Splitting Problem. Unlike a standard Genetic Algorithm, this approach uses a variable chromosome length to represent the vertices that split the graph and a dynamic population size. Two String Length Reduction Methods to reduce the string length and two Stepping Methods to explore the search space have been developed. Combinations of these four methods have been studied and conclusions are drawn. A parallel version of the sequential Genetic Algorithm has been developed. It uses a fully distributed scheme to assign different string lengths to processors. A ring exchange method is used in order to exchange good individuals between processors. Almost linear speed-up and two cases of super linear speed-up are reported

Genetic Algorithms for Vertex Splitting in DAGs

Directed Acyclic Graphs are often used to model circuits and networks. The path length in such Directed Acyclic Graphs represents circuit or network delays. In the vertex splitting problem, the objective is to determine a minimum number of vertices from the graph to split such that the resulting graph has no path of length greater than a given δ. The problem has been proven to be NP-hard. A Genetic Algorithm is used to solve the DAG Vertex Splitting Problem. This approach uses a variable string length to represent the vertices that split the graph and a dynamic population size. The focus of this paper is the comparison of two methods to reduce the string length and of two stepping methods to explore the search space. Experimental results have shown that the multiple binary stepping method outperforms the linear stepping method in yielding better solutions

Diagnosis of Malignant Melanoma using a Neural Network

Malignant melanoma is the deadliest form of all skin cancers. Approximately 32,000 new cases of malignant melanoma were diagnosed in 1991, with approximately 80 percent of patients expected to survive five years [1], Fortunately, if detected early, even malignant melanoma may be treated successfully. Thus, in recent years, there has been a rising interest in the automated detection and diagnosis of skin cancer, particularly malignant melanoma [2]. In this thesis, a novel neural network approach for the automated distinction of melanoma from three benign categories of tumors which exhibit melanoma-like characteristics is presented. The approach is based on devising new and discriminant features which are used as inputs to an artificial neural network for classification of tumor images as malignant or benign. Promising results have been obtained using this method on real skin cancer images

Visual Inspection Algorithms for Printed Circuit Board Patterns A SURVEY

The importance of the inspection process has been magnified by the requirements of the modern manufacturing environment. In electronics mass-production manufacturing facilities, an attempt is often made to achieve 100 % quality assurance of all parts, subassemblies, and finished goods. A variety of approaches for automated visual inspection of printed circuits have been reported over the last two decades. In this survey, algorithms and techniques for the automated inspection of printed circuit boards are examined. A classification tree for these algorithms is presented and the algorithms are grouped according to this classification. This survey concentrates mainly on image analysis and fault detection strategies, these also include the state-of-the-art techniques. Finally, limitations of current inspection systems are summarized

Neural Network Diagnosis of Malignant Melanoma from Color Images

Malignant melanoma is the deadliest form of all skin cancers. Approximately 32,000 new cases of malignant melanoma were diagnosed in 1991, with approximately 80 percent of patients expected to survive five years [1]. Fortunately, if detected early, even malignant melanoma may be treated successfully. Thus, in recent years, there has been a rising interest in the automated detection and diagnosis of skin cancer, particularly malignant melanoma [2]. In this paper, we present a novel neural network approach for the automated separation of melanoma from three other benign categories of tumors which exhibit melanoma-like characteristics. Our approach is based on devising new and discriminant features which are used as inputs to an artificial neural network for classification of tumor images as malignant or benign. We have obtained promising results using our method on real skin cancer images

Identifying Character Non-Independence in Phylogenetic Data using Data Mining Techniques

Undiscovered relationships in a data set may confound analyses, particularly those that assume data independence. Such problems occur when characters used for phylogenetic analyses are not independent of one another. A main assumption of phylogenetic inference methods such as maximum likelihood and parsimony is that each character serves as an independent hypothesis of evolution. When this assumption is violated, the resulting phylogeny may not reflect true evolutionary history. Therefore, it is imperative that character non-independence be identified prior to phylogenetic analyses. To identify dependencies between phylogenetic characters, we applied three data mining techniques: 1) Bayesian networks, 2) decision tree induction, and 3) rule induction from coverings. We briefly discuss the main ideas behind each strategy, show how each technique performs on a small sample data set, and apply each method to an existing phylogenetic data set. We discuss the interestingness of the results of each method, and show that, although each method has its own strengths and weaknesses, rule induction from coverings presents the most useful solution for determining dependencies among phylogenetic data at this time

X.500 Directory Service Support for Electronic Mail

One of the difficult problems on the Internet is finding the electronic mail addresses of users. In practice, there are some indirect ways of finding these addresses such as the finger program in UNIX, but almost all of these methods require the user to know the exact host name of the destination. What is most desirable is an automated mechanism which provides the e-mail addresses of users if some minimal information about the destination site is known. This thesis describes the design of such a directory service support system, based on the X.500 Series of CCITT Recommendation, for the elm electronic mail facility in the UNIX environment. The purpose of the system is to provide easy access to the electronic mail addresses of users, without requiring the direct use of the X.500 directory system. To facilitate the service, the user only needs to know the approximate name of the person and the location he/she works at. The query is done automatically and the returned e-mail address is used for the “To:” field in the header. In case of multiple hits, the sender has a few options: he can choose between the possible solutions offered by the system, ask for more information about a person or eliminate ambiguous references from the original request. The system was designed and implemented on a Sun SparcStation. Tests were conducted to measure the response time of the system and the results are reported. Work is in progress to provide more functionality and a better user interface

Cartographic Pattern Recognition using Template Matching

In creating digital maps from paper maps, the paper map must first be scanned to produce a raster image, and then converted into vector format. Vector format allows non-graphical cartographic information to be stored along with the graphical objects. At the United States Geological Survey, the conversion from raster to vector format is performed by a commercial software package. The package also attempts to classify the graphical objects based on shape, line patterns, and other information gained from the raster file. Since the package frequently fails to classify a significant percentage of the elements in the map, manual map analysis and classification, a slow and costly process, is necessary. The current project implements template matching in an effort to reduce the amount of manual analysis necessary for hydrography files (files containing all water data from a map). Lines appearing in a hydrography file are either solid (shoreline or perennial streams) or made up of the repeated pattern of a dash and three dots (intermittent streams). The Wise Intermittent Stream Recognition and Detection (WISRD) system has been created to identify lines having the intermittent stream pattern, thereby decreasing the amount of manual editing necessary for hydrography files. The choice of template matching as a pattern recognition technique has proven to be quite beneficial in the classification process. The WISRD system succeeded in significantly reducing the number of unclassified lines upon its completion