Among the layered transition metal dichalcogenides (TMDs) that can form stable two-dimensional crystal structures, molybdenum disulfide (MoS) has been intensively investigated because of its unique properties in various electronic and optoelectronic applications with different band gap energies from 1.29 to 1.9 eV as the number of layers decreases. To control the MoS layers, atomic layer etching (ALE) (which is a cyclic etching consisting of a radical-adsorption step such as Cl adsorption and a reacted-compound-desorption step via a low-energy Ar-ion exposure) can be a highly effective technique to avoid inducing damage and contamination that occur during the reactive steps. Whereas graphene is composed of one-atom-thick layers, MoS is composed of three-atom-thick S-Mo-S layers; therefore, the ALE mechanisms of the two structures are significantly different. In this study, for MoS ALE, the Cl radical is used as the adsorption species and a low-energy Ar ion is used as the desorption species. A MoS ALE mechanism (by which the S, Mo, and S atoms are sequentially removed from the MoS crystal structure due to the trapped Cl atoms between the S layer and the Mo layer) is reported according to the results of an experiment and a simulation. In addition, the ALE technique shows that a monolayer MoS field effect transistor (FET) fabricated after one cycle of ALE is undamaged and exhibits electrical characteristics similar to those of a pristine monolayer MoS FET. This technique is also applicable to all layered TMD materials, such as tungsten disulfide (WS), molybdenum diselenide (MoSe), and tungsten diselenide (WSe).

State-of-the-art 3D morphable model (3DMM) is used widely for 3D face reconstruction based on a single image. However, this method has a high computational cost, and hence, a simplified 3D morphable model (S3DMM) was proposed as an alternative. Unlike the original 3DMM, S3DMM uses only a sparse 3D facial shape, and therefore, it incurs a lower computational cost. However, this method is vulnerable to self-occlusion due to head rotation. Therefore, we propose a solution to the self-occlusion problem in S3DMM-based 3D face reconstruction. This research is novel compared with previous works, in the following three respects. First, self-occlusion of the input face is detected automatically by estimating the head pose using a cylindrical head model. Second, a 3D model fitting scheme is designed based on selected visible facial feature points, which facilitates 3D face reconstruction without any effect from self-occlusion. Third, the reconstruction performance is enhanced by using the estimated pose as the initial pose parameter during the 3D model fitting process. The experimental results showed that the self-occlusion detection had high accuracy and our proposed method delivered a noticeable improvement in the 3D face reconstruction performance compared with previous methods.

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This paper presents a statistical model‐based noise suppression approach for voice recognition in a car environment. In order to alleviate the spectral whitening and signal distortion problem in the traditional decision‐directed Wiener filter, we combine a decision‐directed method with an original spectrum reconstruction method and develop a new two‐stage noise reduction filter estimation scheme. When a tradeoff between the performance and computational efficiency under resource‐constrained automotive devices is considered, ETSI standard advance distributed speech recognition font‐end (ETSI‐AFE) can be an effective solution, and ETSI‐AFE is also based on the decision‐directed Wiener filter. Thus, a series of voice recognition and computational complexity tests are conducted by comparing the proposed approach with ETSI‐AFE. The experimental results show that the proposed approach is superior to the conventional method in terms of speech recognition accuracy, while the computational cost and frame latency are significantly reduced.

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In this paper, alternative dynamic features for speech recognition are proposed. The goal of this work is to improve speech recognition accuracy by deriving the representation of distinctive dynamic characteristics from a speech spectrum. This work was inspired by two temporal dynamics of a speech signal. One is the highly non‐stationary nature of speech, and the other is the inter‐frame change of a speech spectrum. We adopt the use of a sub‐frame spectrum analyzer to capture very rapid spectral changes within a speech analysis frame. In addition, we attempt to measure spectral fluctuations of a more complex manner as opposed to traditional dynamic features such as delta or double‐delta. To evaluate the proposed features, speech recognition tests over smartphone environments were conducted. The experimental results show that the feature streams simply combined with the proposed features are effective for an improvement in the recognition accuracy of a hidden Markov model–based speech recognizer.

This study suggests that the use of rhBMP-2 in combination with or without ADSC is helpful to promote bone regeneration in high-speed distraction osteogenesi s of adult rabbit cranium.

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