Sitka Spruce Quality Estimation using Neural Networks
This paper describes an automated classifier for the 
identification of good wood and knotty wood from 
computer tomography (CT) images of logs. Such a 
system is intended to allow better assessment of saw 
logs before being cut into timber. We describe a new empirical model for the growth of Sitka Spruce (Picea 
Stichensis (Bong, Carr)) whose operation is adapted to 
Irish conditions. The use of Hopfield networks for 2D 
cross-section image reconstruction from CT data 
obtained from the model is investigated. We also used 
a multi-layer feedforward neural network trained with 
fast-backpropagation to identify good wood from 
knotty wood. The Hopfield approach to image 
reconstruction was seen as being unsuitable for 
application with the wood industry. However, the use 
of a feedforward neural network for wood classification produced very promising results when trained on our tree model. It is expected that results from real wood data would be even more accurate.
Geon theory as an account of shape recognition in mind and brain
In a fraction of a second humans are able to comprehend novel images of objects and scenes. Indeed, the human represents the only existence proof that a general shape recognizer is even possible. G...
Photoreflectance characterization of vanadium-doped GaAs layers grown by metalorganic vapor phase epitaxy
Abstract We characterized the vanadium-doped (GaAs:V) layers grown on GaAs substrate by metalorganic vapor phase epitaxy. The conductivity of the samples is checked by the Hall effect measurements, and it is n type with electrons concentration ranging from 2×10 13 to 2×10 17  cm −3 . We used the photoreflectance technique (PR) to study the effect of vanadium doping on the optical properties of GaAs. Our experimental results show that the built-in electric field F and the broadening parameter Γ 0 increase with V-doping concentration. Furthermore, the temperature dependence of the built-in electric field and the broadening parameter of GaAs:V samples have been investigated by PR. It has been observed that these parameters increase with increasing temperature. The decrease of the built-in electric field with decreasing temperature is due to the photovoltage effect.We characterized the vanadium-doped GaAs (GaAs:V) layers grown on GaAs substrate by metalorganic vapor phase epitaxy.