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Full-Text Articles in Electrical and Computer Engineering
Data-Driven Homologue Matching For Chromosome Identification, R. Joe Stanley, James M. Keller, Paul D. Gader, Charles William Caldwell
Data-Driven Homologue Matching For Chromosome Identification, R. Joe Stanley, James M. Keller, Paul D. Gader, Charles William Caldwell
Electrical and Computer Engineering Faculty Research & Creative Works
Karyotyping involves the visualization and classification of chromosomes into standard classes. In "normal" human metaphase spreads, chromosomes occur in homologous pairs for the autosomal classes 1-22, and X chromosome for females. Many existing approaches for performing automated human chromosome image analysis presuppose cell normalcy, containing 46 chromosomes within a metaphase spread with two chromosomes per class. This is an acceptable assumption for routine automated chromosome image analysis. However, many genetic abnormalities are directly linked to structural or numerical aberrations of chromosomes within the metaphase spread. Thus, two chromosomes per class cannot be assumed for anomaly analysis. This paper presents the …
Voice Recognition Using Neural Networks, Ganesh K. Venayagamoorthy, Viresh Moonasar, K. Sandrasegaran
Voice Recognition Using Neural Networks, Ganesh K. Venayagamoorthy, Viresh Moonasar, K. Sandrasegaran
Electrical and Computer Engineering Faculty Research & Creative Works
One solution to the crime and illegal immigration problem in South Africa may be the use of biometric techniques and technology. Biometrics are methods for recognizing a user based on unique physiological and/or behavioural characteristics of the user. This paper presents the results of ongoing work into using neural networks for voice recognition
The General Approximation Theorem, Donald C. Wunsch, Alexander N. Gorban
The General Approximation Theorem, Donald C. Wunsch, Alexander N. Gorban
Electrical and Computer Engineering Faculty Research & Creative Works
A general approximation theorem is proved. It uniformly envelopes both the classical Stone theorem and approximation of functions of several variables by means of superpositions and linear combinations of functions of one variable. This theorem is interpreted as a statement on universal approximating possibilities ("approximating omnipotence") of arbitrary nonlinearity. For the neural networks, our result states that the function of neuron activation must be nonlinear, and nothing else