The sites that doped divalent Eu ions occupy in BaMgAl10O17 were studied by X-ray absorption fine structure (XAFS) measurement. The radial structural function and the Fourier-filtered EXAFS wiggle derived from the observed XAFS spectrum suggested that Eu ions occupy the Beevers-Ross and/or anti-Beevers-Ross sites. Observed XANES spectrum also could be explained by Beevers-Ross site occupation.

We investigated the intercalation of water into BaMgAl10O17:Eu2+ (BAM), a blue phosphor that is used in plasma display panels. The adsorption and desorption characteristics of water with BAM have hysteresis; showing that water is intercalated into BAM. Using thermal analysis techniques, we suggested that water hydrated to barium ions caused oxidation. We found that the water intercalated into BAM played an important role in the oxidation of Eu2+ between 450 and 600, and contributed to a 10% degradation of luminance. In contrast, oxidation due to oxygen is a principal factor in degradation above 600 through baking process in air.

We propose a new associative memory named Multi-Winner Associative Memory (MWAM) and study its bidirectional association properties in this paper. The proposed MWAM has two processes for pattern pairs storage: storage process and recall process. For the storage process, the proposed MWAM can represent a half of pattern pair in the distributed representation layer and can store the correspondence of pattern and its representation using the upward weights. In addition, the MWAM can store the correspondence of the distributed representation and the other half of pattern pair in the downward weights. For the recall process, the MWAM can recall information bidirectionally: a half of the stored pattern pair can be recalled by receiving the other half in the input-output layer for any stored pattern pairs.

This paper describes the error probability of the second order BAM estimated by a computer simulation and an analytical calculation method. The computer simulation suggests that the iterations to retrieve a library pattern almost converge within four times and the difference between once and twice is much larger than that between twice and four times. The error probability at the output of the second iteration is estimated by the analytical method. The effect of the noise bits is also estimated using the analytical method. The BAM with larger n is more robust for the noise. For example, the noise bits of 0.15n cause almost no degradation of the error probability when n is larger than 100. If the error probability of 10-4 is allowable, the capacity of the second order BAM can be increased by about 40% in the presence of 0.15n noise bits when n is larger than 500.

This paper describes relation between the number of library pairs and error probability to have all the pairs as fixed points for second-order bidirectional associative memory (BAM). To estimate accurate error probability, three methods have been compared; (a) Gaussian approximation, (b) characteristic function method, and (c) Hermite Gaussian approximation (proposed by this paper). Comparison shows that Gaussian approximation is valid for the larger numbers of neurons in both two layers than 1000. While Hermite Gaussian approximation is applicable for the larger number of neurons than 30 when Hermite polynomials up to 8th are considered. Capacity of second-order BAM at the fixed error probability is estimated as the function of the number of neurons.

Ba ferrite films were deposited epitaxially on ZnO underlayer from targets with composition of BaO-6.5Fe2O3 at substrate temperature of 600 using the facing targets sputtering apparatus. The gas mixture of Ar and Xe of 0.18 Pa and O2 of 0.02 Pa was used as the sputtering gas and the dependences of crystallographic and magnetic characteristics on the partial Xe pressure PXe(0.0-0.18 Pa) were investigated. Films deposited at various PXe were composed of BaM ferrite and spinel crystallites, and the minimum centerline average roughness Ra of 8.3 nm was obtained at PXe of 0.10 Pa. Since saturation 4πMs of 5.1 kG and perpendicular anisotropy constant Ku1 of 4.23105 Jm-3 were larger than those of bulk BaM ferrite of 4.8 kG and 3.30105 Jm-3, respectively, these films appeared promising for use as perpendicular recording media.

Recently, many researches on associative memories have been made a lot of neural network models have been proposed. Bidirectional Associative Memory (BAM) is one of them. The BAM uses Hebbian learning. However, unless the traning vectors are orthogonal, Hebbian learning does not guarantee the recall of all training pairs. Namely, the BAM which is trained by Hebbian learning suffers from low memory capacity. To improve the storage capacity of the BAM, Pseudo-Relaxation Learning Algorithm for BAM (PRLAB) has been proposed. However, PRLAB needs long learning epochs because of random initial weights. In this paper, we propose Quick Learning for BAM which greatly reduces learning epochs and guarantees the recall of all training pairs. In the proposed algorithm, the BAM is trained by Hebbian learning in the first stage and then trained by PRLAB. Owing to the use of Hebbian learning in the first stage, the weights are much closer to the solution space than the initial weights chosen randomly. As a result, the proposed algorithm can reduce the learning epocks. The features of the proposed algorithm are: 1) It requires much less learning epochs. 2) It guarantees the recall of all training pairs. 3) It is robust for noisy inputs. 4) The memory capacity is much larger than conventional BAM. In addition, we made clear several important chracteristics of the conventional and the proposed algorithms such as noise reduction characteristics, storage capacity and the finding of an index which relates to the noise reduction.