Spectra of the magneto-optical Kerr effect, in a series of ͓Fe (xML͒/Au (xML)͔ N superlattices with integer and noninteger values of x (1рxр15), were measured. For xϭ1 the superlattic forms an L1 0 -type ordered alloy structure. The magneto-optical spectrum of the superlattice exhibits a prominent structure around 4 eV, which shows a systematic shift toward higher energies as x increases (1рxр5). No such structure can be reproduced by an optical calculation assuming a simple stack of thin Fe and Au layers. Ab initio band calculations have shown similar magneto-optical spectra and a similar peak shift with an increase in x (1рxр6). This suggests that electronic structures which differ from that of a simple stack of thin Fe and Au are realized in the superlattices. For noninteger values of x, oscillatory behavior with a period of one monolayer was observed in the low energy region of the magneto-optical spectra.

-Magnetooptical Kerr spectra were measured in [Fc(nML)/ Au(nlvfL)h superlattices with non-intcger \"aluc of n. Although oycrall spectral features are similar to those of integer superlattices, clear differences \yere found in low-energy spectra, Spectra of the off-diagonal conductiYity tensor clement multiplied by the angular frequency, O)CJ n were calculated. An oscillatory beha\'ior was found in the absolute \"alue of (t)CJ xy at the photon energy of 1.5 eV '

In recent years, speech recognition under the noisy environment is one of the very important technologies. This study improves a speech recognition method for the signals under the noisy environment using method of thresholds and emphasizing wavelet coefficients for clean signal data. Noisy speech recognitions are extremely difficult problem. To analyze noise problem, in general, the majority of people have used the Fourier analysis. But the Fourier transform reveals only the frequency information. The general noise filters reduce specific frequency band contained both signal and noise. It is difficult to eliminate only noise component from a signal containing noise components. To overcome this difficulty, we applied the wavelet analysis. In this study, we improve the speech recognition under the noisy environment by bringing it close to reference data and noisy input data by modifying the spectrum by the wavelet transform using thresholds and emphasizing methods for reference clean speech data. We apply this method using wavelet transform to the modification of spectral envelope shape. As a result, noisy speech recognition rate is improved by this method using wavelet transform for clean speech data.

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