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Increasing the efficiency of spin–orbit torque (SOT) is of great interest in spintronics devices because of its application to the non-volatile magnetic random access memory and in-logic memory devices. Accordingly, there are several studies to alter the magnetic properties and reduce the SOT switching current with helium ion irradiation, but previous researches are focused on its phenomenological changes only. Here, the authors observe the reduction of switching current and analyze its origins. The analyzed major reasons are improved spin Hall angle represented as the changed resistivity of heavy metal layer and the reduction of surface anisotropy energy at interface between heavy metal and ferromagnet. It is confirmed that almost linear relation between changed SHA and Pt resistivity by helium ion irradiation, which is attributed because of the increase in the scattering sources induced by structural distortion during ion penetration. From the calculated power consumption ratio based on the derived parameter, the requiring power decreases according to the degree of ion irradiation. Our results show that helium ion penetration induced layer and interfacial disturbance affects SOT induced magnetization switching current reduction and may provide possibility about helium ion irradiation based superior SOT device engineering.

The quality of speech reproduced using conventional single-channel speech enhancement schemes is seriously affected by acoustic noise level. Nonacoustic sensors have the ability to reveal certain speech attributes that are lost in noisy acoustic signals. This study validated the use of ultrasonic doppler frequency shifts caused by facial movements for enhancing audio speech contaminated by high levels of acoustic noise. A 40 kHz ultrasonic beam is incident to a speaker’s face. The received signals were first demodulated and converted to a spectral feature parameter. The spectral feature derived from the ultrasonic Doppler signal (UDS) was concatenated with spectral features from noisy speech, which were then used to estimate the magnitude of the spectrum of clean speech. A nonlinear regression approach was employed in this estimation where the relationship between audio-UDS features and the corresponding clean speech is represented by deep neural networks (DNN). The feasibility of the proposed enhancement method was tested on a 1 h audio-UDS corpus and four different types of noise data. The results showed that, both objectively and subjectively, the best performance was obtained when the audio and UDS were used cooperatively. A correlation analysis was also carried out to investigate the usefulness of multi-directional ultrasonic sensing. The results showed that the performance was affected by the number of the adopted UDS channels, particularly in cases of low levels of SNRs.

Reliable, user-friendly and convenient sensing is highly desirable when the continuous monitoring of food intake is necessary. In this paper, food intake monitoring was during the processes of chewing and swallowing. Acoustic Doppler sonar (ADS) detected chewing and swallowing events that were non-contact and free from acoustic interference. When a 40 kHz ultrasonic beam was focused on the lower jaw and neck, movements of the chin and neck cause Doppler frequency shifts and an amplitude envelope modulation of ultrasonic signals. Hence, it was possible to detect chewing and swallowing events using Doppler frequency shifts in the received ultrasound signals. To prevent suspicious chew events caused by talking from being recognized as food intake events, the log-filter bank energy of the voice band was also taken into consideration. Automatic detection of chewing and swallowing events was achieved via an artificial neural network. The experimental results showed that the proposed ADS-based food intake detection method yielded promising results with maximum recognition rates of 91.4% and 78.4% for chewing and swallowing, respectively. As a result, it was confirmed that the proposed food intake detection method using ultrasonic Doppler yielded high rates of recognition without discomfort to the user from continuous skin contact.

A large amount of parallel training corpus is necessary for robust, high-quality voice conversion. However, such parallel data may not always be available. This letter presents a new voice conversion method that needs no parallel speech corpus, and adopts a restricted Boltzmann machine (RBM) to represent the distribution of the spectral features derived from a target speaker. A linear transformation was employed to convert the spectral and delta features. A conversion function was obtained by maximizing the conditional probability density function with respect to the target RBM. A feasibility test was carried out on the OGI VOICES corpus. Results from the subjective listening tests and the objective results both showed that the proposed method outperforms the conventional GMM-based method.

With the development of portable ultrasound imaging systems, prosthetic hands using ultrasound images (UIs) of the human forearm has been studied. Since the features for the estimation of finger positions were heuristically determined from the corresponding UIs in the previous studies, optimal performance was not guaranteed. In this paper, we propose a method to ensure optimal performance for the estimation of finger movements using convolutional neural networks (CNN). The experimental results showed that the proposed method revealed the improved performance over previous methods. The best performance of the proposed method was RMSE of 0.0571. A validation test was also carried out to verify the usefulness of the UI features resulted from the trained CNN, using Gradient-weighted Class Activation Mapping (Grad-CAM).

Continuous recognition of ingested foods without user intervention is very useful for the pre-screening of obesity and diet-related disease. An automatic food recognition method that combines the two modalities of audio and ultrasonic signals (US) is proposed in this study. Under a noise-free environment, classification accuracy of an audio-only recognizer is generally higher than that of US-only recognizers, but the performance of US recognizers is unaffected by acoustic noise levels. In the recognition system presented herein, the likelihood score of the audio-US feature was given by a linear combination of class-conditional observation log-likelihoods for two classifiers, using the appropriate weights. We developed a weighting process adaptive to signal-to-noise ratios (SNRs). The main objective here involves determining the optimal SNR classification boundaries and constructing a set of optimum stream weights for each SNR class. A feasibility test was conducted to verify the usefulness of the proposed method by conducting recognition experiments on seven types of food. The performance was compared with conventional methods that use in-ear and throat microphones. The proposed method yielded remarkable levels of recognition performance of 90.13% for artificially added noise and 89.67% under actual noisy environments, when the SNR ranged from 0 to 20 dB.

Spin-orbit torques arising from the spin-orbit coupling of non-magnetic heavy metals allow electrical switching of perpendicular magnetization. However, the switching is not purely electrical in laterally homogeneous structures. An extra in-plane magnetic field is indeed required to achieve deterministic switching, and this is detrimental for device applications. On the other hand, if antiferromagnets can generate spin-orbit torques, they may enable all-electrical deterministic switching because the desired magnetic field may be replaced by their exchange bias. Here we report sizeable spin-orbit torques in IrMn/CoFeB/MgO structures. The antiferromagnetic IrMn layer also supplies an in-plane exchange bias field, which enables all-electrical deterministic switching of perpendicular magnetization without any assistance from an external magnetic field. Together with sizeable spin-orbit torques, these features make antiferromagnets a promising candidate for future spintronic devices. We also show that the signs of the spin-orbit torques in various IrMn-based structures cannot be explained by existing theories and thus significant theoretical progress is required.

In voice conversion (VC), it is highly desirable to obtain transformed speech signals that are perceptually close to a target speaker&rsquo, s voice. To this end, a perceptually meaningful criterion where the human auditory system was taken into consideration in measuring the distances between the converted and the target voices was adopted in the proposed VC scheme. The conversion rules for the features associated with the spectral envelope and the pitch modification factor were jointly constructed so that perceptual distance measurement was minimized. This minimization problem was solved using a deep neural network (DNN) framework where input features and target features were derived from source speech signals and time-aligned version of target speech signals, respectively. The validation tests were carried out for the CMU ARCTIC database to evaluate the effectiveness of the proposed method, especially in terms of perceptual quality. The experimental results showed that the proposed method yielded perceptually preferred results compared with independent conversion using conventional mean-square error (MSE) criterion. The maximum improvement in perceptual evaluation of speech quality (PESQ) was 0.312, compared with the conventional VC method.

A voice transformation (VT) method that can make the utterance of a source speaker mimic that of a target speaker is described. Speaker individuality transformation is achieved by altering four feature parameters, which include the linear prediction coefficients cepstrum (LPCC), Δ LPCC, LP-residual and pitch period. The main objective of this study involves construction of an optimal sequence of features selected from a target speaker’s database, to maximize both the correlation probabilities between the transformed and the source features and the likelihood of the transformed features with respect to the target model. A set of two-pass conversion rules is proposed, where the feature parameters are first selected from a database then the optimal sequence of the feature parameters is then constructed in the second pass. The conversion rules were developed using a statistical approach that employed a maximum likelihood criterion. In constructing an optimal sequence of the features, a hidden Markov model (HMM) with global control variables (GCV) was employed to find the most likely combination of the features with respect to the target speaker’s model. The effectiveness of the proposed transformation method was evaluated using objective tests and formal listening tests. We confirmed that the proposed method leads to perceptually more preferred results, compared with the conventional methods.

The present study tested a new stereo playback system that effectively cancels cross-talk signals at an arbitrary listening position. Such a playback system was implemented by integrating listener position tracking techniques and crosstalk cancellation techniques. The entire listening space was partitioned into a number of non-overlapped cells and a crosstalk cancellation filter was assigned to each cell. The listening space partitions and the corresponding crosstalk cancellation filters were constructed by maximizing the average channel separation ratio (CSR). Since the proposed method employed cell-based crosstalk cancellation, estimation of the exact position of the listener was not necessary. Instead, it was only necessary to determine the cell in which the listener was located. This was achieved by simply employing an artificial neural network (ANN) where the time delay to each pair of microphones was used as the ANN input and the ANN output corresponded to the index of cells. The experimental results showed that more than 95% of the experimental listening space had a CSR ≥ 10 dB when the number of clusters exceeded 12. Under these conditions, the correlation between the true directions of the virtual sound sources and the directions recognized by the subjects was greater than 0.9.

In this paper, a limited-view CT image reconstruction method was studied to reduce the scan times and the X-ray dose for the patients. To reduce streak artifacts which is caused by insufficient number of views, we introduce a sinogram interpolation method based on image matching. Image matching is achieved using the characteristics of the neighboring views including intensity, gradient and distance between the pixels. Interpolation is performed using the image matching results.. A numerical phantom and Al-acryl phantom were used for evaluating the effectiveness of the proposed interpolation method. The results showed that streak artifacts were reduced in the reconstructed images while the details of the images were preserved. Moreover, maximum 5% improvements in terms of PSNR were observed.

A voice transformation (VT) method that can make the utterance of a source speaker mimic that of a target speaker is described. Speaker individuality transformation is achieved by altering three feature parameters, which include the LPC cepstrum, pitch period and gain. The main objective of this study involves construction of an optimal sequence of features selected from a target speaker’s database, to maximize both the correlation probabilities between the transformed and the source features and the likelihood of the transformed features with respect to the target model. A set of two-pass conversion rules is proposed, where the feature parameters are first selected from a database then the optimal sequence of the feature parameters is then constructed in the second pass. The conversion rules were developed using a statistical approach that employed a maximum likelihood criterion. In constructing an optimal sequence of the features, a hidden Markov model (HMM) was employed to find the most likely combination of the features with respect to the target speaker’s model. The effectiveness of the proposed transformation method was evaluated using objective tests and informal listening tests. We confirmed that the proposed method leads to perceptually more preferred results, compared with the conventional methods.

In binaural synthesis, in order to realize more precise and accurate spatial sound, it would be desirable to measure a large number of the head-related transfer functions (HRTFs) in various directions. To reduce the size of the HRTFs, interpolation is often employed, where the HRTF for any direction can be obtained by a limited number of the representative HRTFs. In this paper, it is determined which distortion measure for interpolation of the HRTFs in the horizontal plane is most suitable for predicting audible differences in sound location. Four kinds of HRTF sets, measured using three human heads and one mannequin (KEMAR), were prepared for this study. Using various objective distortion criteria, the differences between interpolated and measured HRTFs were computed. These were then related to the results from the listening tests through receiver operator characteristic (ROC) curves. The results of the present study indicated that for the HRTF sets measured from three human heads, the best predictor of performance was obtained using the distortion measurement computed from the mel-cepstral coefficients, whereas the distortion measurement associated with interaural time delay predicted audible differences in sound location reasonably well for the KEMAR HRTF set. A feasibility test was conducted to verify the usefulness of the selected distortion measurement.

In the present study, a new stereophonic playback system was proposed, where the cross-talk signals would be reasonably cancelled at an arbitrary listener position. The system was composed of two major parts: the listener position tracking part and the sound rendering part. The position of the listener was estimated using acoustic signals from the listener (i.e. voice or hand-clapping signals). A direction of arrival (DOA) algorithm was adopted to estimate the directions of acoustic sources where the room reverberation effects were taken into consideration. A Crosstalk cancellation filter was designed using a free-field model. To determine the maximum tolerable shift of the listener position, a quantitative analysis of the channel separation ratio according to the displacement of the listener position was performed. Prototype hardware was implemented using a microprocessor board, a DSP board, a multi-channel ADC board and an analog frontend. The results showed that the average mean square error between the true direction of a listener and the estimated direction was about 5 degrees. More than 80% of the tested subjects indicated that better stereo images were obtained by the proposed system, compared with the non-processed signals.

Myoelectric signals (MESs) from the speaker's mouth region have been successfully shown to improve the noise robustness of automatic speech recognizers (ASRs), thus promising to extend their usability in implementing noise-robust ASR. In the recognition system presented herein, extracted audio and facial MES features were integrated by a decision fusion method, where the likelihood score of the audio-MES observation vector was given by a linear combination of class-conditional observation log-likelihoods of two classifiers, using appropriate weights. We developed a weighting process adaptive to SNRs. The main objective of the paper involves determining the optimal SNR classification boundaries and constructing a set of optimum stream weights for each SNR class. These two parameters were determined by a method based on a maximum mutual information criterion. Acoustic and facial MES data were collected from five subjects, using a 60-word vocabulary. Four types of acoustic noise including babble, car, aircraft, and white noise were acoustically added to clean speech signals with SNR ranging from -14 to 31 dB. The classification accuracy of the audio ASR was as low as 25.5%. Whereas, the classification accuracy of the MES ASR was 85.2%. The classification accuracy could be further improved by employing the proposed audio-MES weighting method, which was as high as 89.4% in the case of babble noise. A similar result was also found for the other types of noise.

A voice transformation method which changes the source speaker's utterances so as to sound similar to those of a target speaker is described. Speaker individuality transformation is achieved by altering the LPC cepstrum, average pitch period and average speaking rate. The main objective of the work involves building a nonlinear relationship between the parameters for the acoustical features of two speakers, based on a probabilistic model. The conversion rules involve the probabilistic classification and a cross correlation probability between the acoustic features of the two speakers. The parameters of the conversion rules are estimated by estimating the maximum likelihood of the training data. To obtain transformed speech signals which are perceptually closer to the target speaker's voice, prosody modification is also involved. Prosody modification is achieved by scaling excitation spectrum and time scale modification with appropriate modification factors. An evaluation by objective tests and informal listening tests clearly indicated the effectiveness of the proposed transformation method. We also confirmed that the proposed method leads to smoothly evolving spectral contours over time, which, from a perceptual standpoint, produced results that were superior to conventional vector quantization (VQ)-based methods

The composite metal hydride MgH 2 –Me x O y powder was synthesized by high energy ball milling (Me x O y = V 2 O 5 , Cr 2 O 3 , Al 2 O 3 , Fe 2 O 3 ). The hydrogen absorption kinetics of composite metal hydride was determined by automated PCT measurements. The poor kinetics of MgH 2 were greatly improved with an addition of oxide. In absorption, the catalytic effects of Al 2 O 3 and Cr 2 O 3 were remarkable at 300 °C with hydrogen capacity up to 4.09 and 4.02 wt%. At 250 and 200 °C, V 2 O 5 showed that the fastest kinetics and hydrogen absorption capacities up to 3.2 and 2.25 wt%.

This letter describes a robust speech recognition system for recognizing fast speech by stretching the length of the utterance in the cepstrum domain. The degree of stretching for an utterance is determined by its rate of speech (ROS), which is based on a maximum likelihood (ML) criterion. The proposed method was evaluated on 10-digits mobile phone numbers. The results of the simulation show that the overall error rate was reduced by 17.8% when the proposed method was employed.

The automatic labeling of a large speech corpus plays an important role in the development of a high-quality Text-To-Speech (TTS) synthesis system. This paper describes a method for the automatic labeling of speech signals, which mainly involves the construction of a large database for a TTS synthesis system. The main objective of the work involves the refinement of an initial estimation of phone boundaries which are provided by an alignment, based on a Hidden Markov Model. A multilayer perceptron (MLP) was employed to refine the phone boundaries. To increase the accuracy of phoneme segmentation, several specialized MLPs were individually trained based on phonetic transition. The optimum partitioning of the entire phonetic transition space and the corresponding MLPs were constructed from the standpoint of minimizing the overall deviation from the hand-labeling position. The experimental results showed that more than 93% of all phone boundaries have a boundary deviation from a reference position smaller than 20 ms. We also confirmed that the database constructed using the proposed method produced results that were perceptually comparable to a hand-labeled database, based on subjective listening tests.

Boron carbide thin coatings were deposited on silicon wafers by DC magnetron sputtering using a B 4 C target with Ar as processing gas. Various amounts of methane gas (CH 4 ) were added in the deposition process to better understand their influence on tribological properties of the coatings. Reciprocating wear tests employing an oscillating friction wear tester were performed to investigate the tribological behaviors of the coatings in ambient environment. The chemical characteristics of the coatings and worn surfaces were studied using X-ray photoelectron spectroscopy (XPS) and Auger electron spectroscopy (AES). It revealed that CH 4 addition to Ar processing gas strongly affected the tribological properties of sputtered boron carbide coating. The coefficient of friction was reduced approximately from 0.4 to 0.1, and wear resistance was considerably improved by increasing the ratio of CH 4 gas component from 0 to 1.2 vol.%. By adding an optimal amount of CH 4 (∼1.2 vol.%) in the deposition process the boron carbide coating exhibited the lowest friction and highest wear resistance.

A new antibacterial agent gemifloxacin was labelled with carbon-14 for studies of pharmacokinetics and metabolism, the label was located in position 3 of the quinolone ring system. The overall radiochemical yield of the 14-step synthesis, starting from [2-14C]sodium acetate was 16.6%, and the radiochemical purity 97.5%. Copyright © 2004 John Wiley & Sons, Ltd.

The 6061 (Al–1.01 wt% Mg–1.07 wt% Si) Al alloy was fabricated by powder metallurgy, and then subjected to equal channel angular pressing. The microstructure and mechanical properties such as microhardness and tensile properties of the equal channel angular pressed P/M 6061 Al alloy were investigated. The P/M 6061 Al alloy had an initial grain size of approximately 20 μm. After two pressings at 373 K using route A, the sample revealed microstructure of subgrain bands with a length of ∼0.8 μm and a width of ∼0.3 μm. The subgrain bands became larger above 1 μm in length and width after two pressings at 573 K. An equiaxed ultra-fine grained structure with the mean grain size of ∼0.5 μm was obtained after four repetitive equal channel angular pressings at 473 K using route A and C. The microhardness of P/M 6061 Al alloys was drastically increased from about 40 to 80 Hv by two repetitive pressings at 373 K. However, the microhardness decreased with increasing the pressing temperature. The tensile strength of 6061Al alloy before the equal channel angular pressing was 95 MPa, whereas it increased to both 248 MPa after two pressings at 373 K and 130 MPa after four pressings at 473 K, which was superior to that of a commercial 6061-O Al alloy.

In text-to-speech synthesis, spectral smoothing is often employed to reduce artifacts at unit-joining points. A context-adaptive smoothing method is proposed in this letter, where the amount of smoothing is determined according to context information. Discontinuities at unit boundaries are predicted by a regression tree, and smoothing factors are computed by using predicted discontinuities and real discontinuities at unit boundaries. Experimental results are presented to demonstrate the effectiveness of the proposed method.

The 6061 Al–10 vol% SiCw composites were prepared by powder metallurgy with the powders having the different sizes, i.e.

The microstructure of ultrafine grained low carbon steel processed with repetitive equal channel angular pressing was investigated. A submicron ferrite grain size of ∼0.2 μm was achieved by pressings of up to 12 passes. Microstructural examination by TEM with SAD pattern on the pressed samples revealed the presence of high density dislocations inside the ferrite grains and ill-defined grain boundaries. These features became more significant as the number of pressings increased. The static annealing of the pressed samples at 753 K up to 24 hrs resulted in a recovery which was associated with the absorption of the dislocations by the grain boundaries. However, the recovery was inhibited as the number of pressings increased. The annealing process also led to the precipitation of cementite particles in ferrite colonies. The presence of precipitated particles inside the ferrite grains enhanced the microstructural stability of the low carbon steel at elevated temperatures.

Previous studies have shown that a concatenative speech synthesis system with a large database produces more natural sounding speech. We apply this paradigm to the design of improved very low bit rate speech coders (sub 1000 b/s). The proposed speech coder consists of unit selection, prosody coding, prosody modification and waveform concatenation. The encoder selects the best unit sequence from a large database and compresses the prosody information. The transmitted parameters include unit indices and the prosody information. To increase naturalness as well as intelligibility, two costs are considered in the unit selection process: an acoustic target cost and a concatenation cost. A rate-distortion-based piecewise linear approximation is proposed to compress the pitch contour. The decoder concatenates the set of units, and then synthesizes the resultant sequence of speech frames using the harmonic+noise model (HNM) scheme. Before concatenating units, prosody modification which includes pitch shifting and gain modification is applied to match those of the input speech. With single speaker stimuli, a comparison category rating (CCR) test shows that the performance of the proposed coder is close to that of the 2400-b/s MELP coder at an average bit rate of about 800-b/s during talk spurts.

The synthesis of 14 C-2-amino-9-(3-hydroxymethyl-4-isopropoxycarbonyloxybut-1-yl)purine from [1- 14 C] diethyl malonate is described. The overall radiochemical yield of the product in a nine-step sequence was 16.1%, and the compound's radiochemical purity was 98.5%

This letter addresses a temporal decomposition (TD) technique that is based on a rate-distortion criterion. In the proposed TD scheme, a set of interpolation functions is constructed from a given training corpus, and the optimum target points are found in the sense of minimizing, not only spectral distortion, but also bit rates. The results of the simulation show that an average spectral distortion of about 1.4 dB can be achieved at an average bit rate of about 8 bits/frame.

A novel adaptive stereo sound system is proposed. The proposed scheme consists of two parts: listener position tracking using self-generating sound signals, and space skew/crosstalk cancelation. To verify the effectiveness of the proposed system, a positioning accuracy test and a subjective listener's test were carried out. It was shown that the probability of a mean squared positioning error of less than 0.07m2 ranged from 70% to 90% when hand-clap signals were used in a small room. The results from the subjective listening test showed that 71% of the total stimuli were perceived as coming from the desired virtual location, regardless of the listener's position.

We investigate boron transient enhanced diffusion (TED) and series resistance in SiGe/Si heterojunction channel pMOSFET. The stress gradient at the SiGe/Si interface near the gate edge in high Ge concentrations are found to determine boron TED as well as extension junction shape, which has a significant impact on the parasitic LDD and source/drain (S/D) series resistance. In addition, high Ge concentrations in the epitaxial SiGe layer on top of Si substrate result in a high sheet resistance during a 1000°C/5s rapid thermal processing (RTP), which is mainly due to alloy scattering and interface roughness scattering.

It is well known that a strong relationship exists between human voices and the movement of articulatory facial muscles. In this paper, we utilize this knowledge to implement an automatic speech recognition scheme which uses solely surface electromyogram (EMG) signals. The sequence of EMG signals for each word is modelled by a hidden Markov model (HMM) framework. The main objective of the work involves building a model for state observation density when multichannel observation sequences are given. The proposed model reflects the dependencies between each of the EMG signals, which are described by introducing a global control variable. We also develop an efficient model training method, based on a maximum likelihood criterion. In a preliminary study, 60 isolated words were used as recognition variables. EMG signals were acquired from three articulatory facial muscles. The findings indicate that such a system may have the capacity to recognize speech signals with an accuracy of up to 87.07%, which is superior to the independent probabilistic model.

A newly developed image enhancement algorithm is described in this contribution. The proposed algorithm makes use of the signal subspace method to enhance images corrupted by uncorrelated additive noise. This enhancement is performed by eliminating the noise subspace and estimating clean image from the remaining signal subspace. We propose the block-adaptive Wiener filtering which engages properties of the human visual system to estimate clean image. This criterion enables one to not only preserve the detailed structure of the given image, but to reduce the level of background noise as well. Subjective evaluation tests show the superiority of the method proposed here. In particular, edge blurring effects are noticeably reduced compared to the conventional methods.

A method for the automatic segmentation of speech signals is described. The method is dedicated to the construction of a large database for a Text-To-Speech (TTS) synthesis system. The main issue of the work involves the refinement of an initial estimation of phone boundaries which are provided by an alignment, based on a Hidden Markov Model (HMM). Multi-layer perceptron (MLP) was used as a phone boundary detector. To increase the performance of segmentation, a technique which individually trains an MLP according to phonetic transition is proposed. The optimum partitioning of the entire phonetic transition space is constructed from the standpoint of minimizing the overall deviation from hand labelling positions. With single speaker stimuli, the experimental results showed that more than 95% of all phone boundaries have a boundary deviation from the reference position smaller than 20 ms, and the refinement of the boundaries reduces the root mean square error by about 25%.

We describe a voice transformation method which changes the source speaker's acoustic features to those of a target speaker. In the method acoustic features are divided into two parts, linear and nonlinear parts. Linear parts are characterized by LPC cepstrum coefficients which are obtained from LP analysis. The nonlinear part, which represents the excitation signal, is modelled by the long-delay nonlinear predictor using a neural net. Conversion rules for the excitation signal are generated by the average pitch ratio and the mapping codebook, and those for LPC cepstrum coefficients are based on the orthogonal vector space conversion. In addition, the spectral envelope compensation is proposed to correct spectral distortion. In the transformed speech a listening test shows that the proposed method makes it possible to convert speaker's individuality while maintaining high quality.

Ginsenosides are deglycosylated by intestinal bacteria to active forms after oral administration. The present study demonstrated the pharmacodynamics of 20-O-beta-D-glucopyranosyl-20(S)-protopanaxadiol (M1), an intestinal bacterial metabolite of ginsenosides, and the in vitro and in vivo antitumor activities of M1-metabolites in comparison with M1 using C57BL/6 mice and Wistar rats. M1 was selectively accumulated into the liver soon after its intravenous administration to mice, and mostly excreted as bile; however, some M1 was transformed to fatty acid ester (EM1) in the liver. EM1 was isolated from rats in a recovery dose of approximately 24 mol%. Structural analysis indicated that EM1 comprised a family of fatty acid mono-esters of M1. Because EM1 was not excreted as bile as M1 was, it was accumulated in the liver longer than M1. Although the cytotoxicity of M1 against B16-F10 melanoma cells was attenuated by fatty acid esterification, EM1 inhibited tumor growth more than M1 in vivo. These results suggest that the fatty acid M1 esters may be the real active principles of ginsenosides in the body.

This paper addresses a speech coder which uses a text-to-speech (TTS) synthesis system to achieve very low bit rates (sub 1 kbps). The main issue of the work is the accurate coding of the pitch (f/sub 0/) and gain contours which are principle components of prosody. This is of paramount interest since the correct prosody will increase naturalness and an efficient coding scheme will provide high coding gain. Together with the phonetic transcription, the f/sub 0/ and gain contour constitute the parameters that are necessary for the TTS system to synthesize the speech signal. Piecewise linear approximation is used to code the f/sub 0/ parameter. A technique which minimizes the bit rate while maintaining f/sub 0/ error below a given threshold are described. To obtain both high compression and smoothly changing gain contours, the variance of the signal is averaged over each half phoneme length is transmitted as gain information. With single speaker stimuli, and a priori text transcription information, we obtained natural sounding speech at an average bit rate of about 300 bps.

We theoretically investigate the threshold current for thermally activated switching of a perpendicular magnet by spin-orbit spin torque. Based on the Fokker-Planck equation, we obtain an analytic expression of the switching current, in agreement with numerical result. We find that thermal energy barrier exhibits a quasi-linear dependence on the current, resulting in an almost linear dependence of switching current on the log-scaled current pulse-width even below 10 ns. This is in stark contrast to standard spin torque switching, where thermal energy barrier has a quadratic dependence on the current and the switching current rapidly increases at short pulses. Our results will serve as a guideline to design and interpret switching experiments based on spin-orbit spin torque, 18 pages, 4 figures

Replacing OMG (old magazine) to ONP (old newspaper) by raising optical property through CaCO3 in-situ precipitation method in white duplex board presents cost reduction and possible drying energy saving. The strength property impairment by the presence of CaCO3 could be supplemented by the fiber furnish treatment or strength polymer addition. In CaCO3 in-situ precipitation of ONP, it was found from morphological study using FlowCAM, an image analyzer, that most of calcium carbonate were formed on the fines, and made the size of the fines larger. For the case of forming calcium carbonate only on the fractionated fines, the size of the fines were the biggest, and there were more clean surface areas available for bonding for the fractionated long fibers when fractionated fibers and fines were regrouped to make paper.

We theoretically investigate the switching of a perpendicular magnetic layer by in-plane charge current due to the spin Hall effect. We find that, in the high damping regime, the threshold switching current is independent of the damping constant, and is almost linearly proportional to both effective perpendicular magnetic anisotropy field and external in-plane field applied along the current direction. We obtain an analytic expression of the threshold current, in excellent agreement with numerical results. This expression can be used to determine the physical quantities associated with spin Hall effect, and to design relevant magnetic devices based on the switching of perpendicular magnetic layers., Comment: 16 pages, 3 figures