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Recently, vast research has been under investigation on the role of chirality in magnetization dynamics, an area that currently lacks a comprehensive understanding. To gain further insight into the importance of chirality, we explore the effects of varying the degree of chirality. To investigate these effects, we have fabricated samples with perpendicular magnetic anisotropy symmetry breaking through local helium ion irradiation with various azimuthal angles and degrees of irradiation. In this system, the spin-orbit torque can induce artificial spin texture, and our azimuthal angle and degree of chirality dependent results reveal a clear cosine dependence and a nonlinear increase, respectively. It implies the system not only follows the energy contribution of interfacial Dzyaloshinskii-Moriya interaction but also has a nonlinear impact depending on anisotropy asymmetry induced chirality differences. These experimental observations are consistent with our theoretical model and micromagnetic simulations, supporting our experimental results. Overall, our findings provide further insights into the role of chirality in magnetization dynamics and may have important implications for the development of future magnetic devices.

In nutrition science, methods that accomplish continuous recognition of ingested foods with minimal user intervention have great utility. Our recent study showed that using images taken at a variety of wavelengths, including ultraviolet (UV) and near-infrared (NIR) bands, improves the accuracy of food classification and caloric estimation. With this approach, however, analysis time increases as the number of wavelengths increases, and there are practical implementation issues associated with a large number of light sources. To alleviate these problems, we proposed a method that used only standard red-green-blue (RGB) images to achieve performance that approximates the use of multi-wavelength images. This method used RGB images to predict the images at each wavelength (including UV and NIR bands), instead of using the images actually acquired with a camera. Deep neural networks (DNN) were used to predict the images at each wavelength from the RGB images. To validate the effectiveness of the proposed method, feasibility tests were carried out on 101 foods. The experimental results showed maximum recognition rates of 99.45 and 98.24% using the actual and predicted images, respectively. Those rates were significantly higher than using only the RGB images, which returned a recognition rate of only 86.3%. For caloric estimation, the minimum values for mean absolute percentage error (MAPE) were 11.67 and 12.13 when using the actual and predicted images, respectively. These results confirmed that the use of RGB images alone achieves performance that is similar to multi-wavelength imaging techniques.

Abstract 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.

Continuous monitoring and recording of the type and caloric content of ingested foods with a minimum of user intervention is very useful in preventing metabolic diseases and obesity. In this paper, automatic recognition of food type and caloric content was achieved via the use of multi-spectral images. A method of fusing the RGB image and the images captured at ultra violet, visible, and near-infrared regions at center wavelengths of 385, 405, 430, 470, 490, 510, 560, 590, 625, 645, 660, 810, 850, 870, 890, 910, 950, 970, and 1020 nm was adopted to improve the accuracy. A convolutional neural network (CNN) was adopted to classify food items and estimate the caloric amounts. The CNN was trained using 10,909 images acquired from 101 types. The objective functions including classification accuracy and mean absolute percentage error (MAPE) were investigated according to wavelength numbers. The optimal combinations of wavelengths (including/excluding the RGB image) were determined by using a piecewise selection method. Validation tests were carried out on 3636 images of the food types that were used in training the CNN. As a result of the experiments, the accuracy of food classification was increased from 88.9 to 97.1% and MAPEs were decreased from 41.97 to 18.97 even when one kind of NIR image was added to the RGB image. The highest accuracy for food type classification was 99.81% when using 19 images and the lowest MAPE for caloric content was 10.56 when using 14 images. These results demonstrated that the use of the images captured at various wavelengths in the UV and NIR bands was very helpful for improving the accuracy of food classification and caloric estimation.

Moderate performance in terms of intelligibility and naturalness can be obtained using previously established silent speech interface (SSI) methods. Nevertheless, a common problem associated with SSI has involved deficiencies in estimating the spectrum details, which results in synthesized speech signals that are rough, harsh, and unclear. In this study, harmonic enhancement (HE), was used during postprocessing to alleviate this problem by emphasizing the spectral fine structure of speech signals. To improve the subjective quality of synthesized speech, the difference between synthesized and actual speech was established by calculating the distance in the perceptual domains instead of using the conventional mean square error (MSE). Two deep neural networks (DNNs) were employed to separately estimate the speech spectra and the filter coefficients of HE, connected in a cascading manner. The DNNs were trained to incrementally and iteratively minimize both the MSE and the perceptual distance (PD). A feasibility test showed that the perceptual evaluation of speech quality (PESQ) and the short-time objective intelligibility measure (STOI) were improved by 17.8 and 2.9%, respectively, compared with previous methods. Subjective listening tests revealed that the proposed method yielded perceptually preferred results compared with that of the conventional MSE-based method.

In voice conversion (VC), it is highly desirable to obtain transformed speech signals that are perceptually close to a target speaker’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.