Zinc oxide is a promising transparent conductive oxide (TCO) and there is unceasing interest in its optical and electrical
properties for the last decades. In this paper, ZnO thin films modified by various additives such as erbium, vanadium and
aluminum were fabricated using a sol-gel process and their electrical resistivity and surface morphology were
investigated in terms of annealing conditions. Stable ZnO solutions containing different additives were synthesized by
using 2-methoxyethanol as a solvent and monoethanolamine as a stabilizer. The electrical resistivity of ZnO films was
found to be controlled by both doping concentration and annealing condition. Relatively lower electrical resistivity was
achieved for the ZnO films doped with ~ 0.3 mol% Er, 0.3mol% Al or 0.03~0.1 mol % V after a post-annealing at 550 oC
for 1 h in N<sub>2</sub>/H<sub>2</sub>. All the films deposited on glass exhibited very high transmittance of 90~97% within the visible
wavelength region. This work was mainly focused on the overall pictures about the relationship between the electrical
and optical performances and the processing variables such as doping species and concentrations.
As development of digital technology, many kinds of multimedia data are used variously and requirements for effective use by user are increasing. In order to transfer information fast and precisely what user wants, effective retrieval method is required. As existing multimedia data are impossible to apply the MPEG-1, MPEG-2 and MPEG-4 technologies which are aimed at compression, store and transmission. So MPEG-7 is introduced as a new technology for effective management and retrieval for multimedia data. In this paper, we extract
content-based features using color descriptor among the MPEG-7 standardization visual descriptor, and reduce feature data applying PCA(Principal Components Analysis) technique. We remodel the cerebral cortex and hippocampal neural networks as a principle of a human's brain and it can label the features of the image-data which are inputted according to the order of hippocampal neuron structure to reaction-pattern according to the adjustment of a good impression in Dentate gyrus region and remove the noise through the auto-associate- memory step in the CA3 region. In the CA1 region receiving the information of the CA3, it can make long-term or short-term memory learned by neuron. Hippocampal neural network makes neuron of the neural network separate and combine dynamically, expand the neuron attaching additional information using the synapse and add new features according to the situation by user's demand. When user is querying, it compares feature value stored in long-term memory first and it learns feature vector fast and construct optimized feature. So the speed of index and retrieval is fast. Also, it uses MPEG-7 standard visual descriptors as content-based feature value, it improves retrieval efficiency.