This thesis describes our novel methods for data clustering, graph characterizing and image matching. In Chapter 3, our main contribution is the M1NN agglomerative clustering method with a new parallel merging algorithm. A cluster characterizing quantity is derived from the path-based dissimilarity measure. In Chapter 4, our main contribution is the modified log-likelihood model for quantitativ...

This paper is proposed the face recognition method using PCA with neural network back error propagation learning algorithm .In this paper a feature is extracted using principal component analysis and then classification by creation of back propagation neural network. We run our algorithm for face recognition application using principal component analysis, neural network and also calculate its p...

1 Research supported by NIH/NINDS Grant HL65425 Abstract--In this paper we present a new approach for clustering of time-sequence imaging data. The clustering metric used is the normalized cross-correlation, also known as similarity. The main advantage of this metric over the more-traditional Euclidean distance, is that it depends on the signal’s shape rather than its amplitude. Under an assump...

In this paper we present a new approach for clustering data. The clustering metric used is the normalized crosscorrelation, also known as similarity, instead of the traditionally used Euclidean distance. The main advantage of this metric is that it depends on the signal shape rather than its amplitude. Under an assumption of an exponential probability model that has several desirable properties...

To find out the role of the wiring cost in the organization of the neuronal network of the nematode Caenorhabditis elegans, we build the spatial neuronal map of C. elegans based on geometrical positions of neurons. We show that the number of interneuronal connections of the Euclidean length d decays exponentially with d, implying that the wiring cost, defined as the sum of the interneuronal dis...

Those street trees which dangerously overhang the sidewalks and public roads may distract visibility of road signs and lamps or even injure pedestrians. Monitor vegetation encroachment using mobile laser system is one efficient measure for vegetation growth management and preventing accidents above under low labor costs. This paper presents a new workflow for automated detection of street trees...

A computer code and algorithm are developed for the computer perception of molecular symmetry. The code generates and uses the Euclidian distance matrices of molecular structures to generate the permutationinversion group of the molecule. The permutation-inversion group is constructed as the automorphism group of the Euclidian distance matrix. Applications to several molecular structures and fu...

In molecular biology, the issue of quantifying the similarity between two biological sequences is very important. Past research has shown that word-based search tools are computationally efficient and can find some new functional similarities or dissimilarities invisible to other algorithms like FASTA. Recently, under the independent model of base composition, Wu, Burke, and Davison (1997, Biom...

A simple methodology of multi-dimensional vector analysis for the comparison of the academic performance and the openness of university networks of the identical dimension was developed, which is illustrated by the example of the leading universities in the Czech Republic and Germany. In order to make this comparison, proximity measures were introduced with an arbitrary normalized vector of ind...

We present an efficient algorithm to find a realization of a (full) n × n squared Euclidean distance matrix in the smallest possible dimension. Most existing algorithms work in a given dimension: most of these can be transformed to an algorithm to find the minimum dimension, but gain a logarithmic factor of n in their worstcase running time. Our algorithm performs cubically in n (and linearly w...