The amorphous Co–Fe–P and crystalline Co fine needles with diameter 0.1–0.2 μm aspect ratio more than 100 were prepared successfully by applying the dc electrodeposition to the anodization technique and their demagnetizing factors were measured. It was found that the demagnetizing factor of film with needles decreases with increasing aspect ratio and tends to take low constant values around 0.07–0.08. To obtain the low demagnetizing factor by using the shape anisotropy of needle, it was estimated that the aspect ratio more than 100 is needed. The equivalent demagnetizing factor defined by a ratio of a field needed to achieve saturation to the averaged flux density is estimated around 0.6 for the film with Co–Fe–P amorphous needles with 0.1 μm diameter.

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Recycling high-purity rare earths from the waste in fluorescent lamps will assist in preserving rare earth resources. However, the present recovery from the waste in fluorescent lamps is very low at around 30%, and most waste fluorescent lamps are reclaimed. In this study we attempted to reduce one cause of difficulty in recycling lamps, namely the identification of rare earths in plastic-covered lamps, which is conventionally executed by human operators. Using the Acoustic Emission (AE) signal of the collision sound between lamps and an AE sensor head, we first carried out waveform analysis, and frequency analysis using the discrete Fourier transform. We extracted six feature values for each analysis and differentiated the plastic-covered lamps using thresholds. The least error rates were 4.85% and 6.67% for the waveform and frequency analyses, respectively. Second, we applied discriminant analysis on the six feature values from each analysis, resulting in error rates of 3.03% and 5.45%, respectively. Third, we extracted six feature values from the time-frequency analysis using the discrete Wavelet transform and Fractal dimensioning, and applied discriminant analysis on the six feature values, resulting in an error rate of 4.24%. Finally, we examined all the results from the three analyses to find the best combination of feature values, applied discriminant analysis to 12 feature values from the time analysis and time-frequency analysis, and succeeded in reducing the error rate to 0%.