In recent years, more and more people suffer from voice-related diseases. Given the limitations of current pathological speech conversion methods, that is, a method can only convert a single kind of pathological voice. In this study, we propose a novel Encoder-Decoder Generative Adversarial Network (E-DGAN) to generate personalized speech for pathological to normal voice conversion, which is suitable for multiple kinds of pathological voices. Our proposed method can also solve the problem of improving the intelligibility and personalizing custom speech of pathological voices. Feature extraction is performed using a mel filter bank. The conversion network is an encoder-decoder structure, which is used to convert the mel spectrogram of pathological voices to the mel spectrogram of normal voices. After being converted by the residual conversion network, the personalized normal speech is synthesized by the neural vocoder. In addition, we propose a subjective evaluation metric named “content similarity” to evaluate the consistency between the converted pathological voice content and the reference content. The Saarbrücken Voice Database (SVD) is used to verify the proposed method. The intelligibility and content similarity of pathological voices are increased by 18.67% and 2.60%, respectively. Besides, an intuitive analysis based on a spectrogram was done and a significant improvement was achieved. The results show that our proposed method can improve the intelligibility of pathological voices and personalize the conversion of pathological voices into the normal voices of 20 different speakers. Our proposed method is compared with five other pathological voice conversion methods, and our proposed method has the best evaluation results.

In this work, thin SiO₂ insulating layers were generated on the top and bottom surfaces of single-crystalline silicon plates (n type) by thermal oxidation to obtain an insulator/semiconductor/insulator (ISI) multilayer structure. X-ray diffraction (XRD) pattern and scanning electron microscope (SEM) pictures implied that all of the synthesized SiO₂ layers were amorphous. By controlling the thermal oxidation times, we obtained SiO₂ layers with various thicknesses. The dielectric properties of silicon plates with different thicknesses of SiO₂ layers (different thermal oxidation times) were measured. The dielectric properties of all of the single-crystalline silicon plates improved greatly after thermal oxidation. The dielectric constant of the silicon plates with SiO₂ layers was approximately 10⁴, which was approximately three orders more than that of the intrinsic single-crystalline silicon plate (11.9). Furthermore, both high permittivity and low dielectric loss (0.02) were simultaneously achieved in the single-crystalline silicon plates after thermal oxidation (ISI structure).

In the context of autonomous driving, monocular 3D detection is regarded as a fundamental and essential task due to its convenience, speed, and low cost. However, the lack of depth information in monocular images presents significant challenges for predicting object 3D information. Although existing methods address this issue using LiDAR guidance or pre-trained depth estimators, their substantial computational resource requirements limit scalability in real-world applications. In this letter, we propose a novel monocular 3D object detector with stereo guidance, called MonoSG. It simulates human visual perception by using stereo images during training to guide learning and retrieves the right-view and depth information from monocular images during inference for accurate 3D detection. The Stereo Guidance Cross Attention Module (SG-CAM) is designed to fuse binocular image information. Intra-view features are extracted from binocular images, and cross-attention is computed from the left to the right view. Then, the cross-attention features are fused with the intra-view features of the left view, enabling stereo guidance for MonoSG. To better adapt to different data distributions and improve the generalization ability of MonoSG, the Stereo Guidance Auxiliary Labels (SG-AL) are introduced for each object of stereo images, with DIoU3D proposed as the label score. Furthermore, the SG-AL score loss is proposed to guide the MonoSG that can reduce gradient variance, facilitate network convergence, and mitigate the issue of insufficient depth information. Comprehensive experiments on the KITTI dataset validate the effectiveness of our method, demonstrating superior performance, particularly on low-resolution images.

Sulcus vocalis is an indentation parallel to the edge of vocal fold, which may extend into the cover and ligament layer of the vocal fold or deeper. The effects of sulcus vocalis depth on phonation and the vocal cord vibrations are investigated in this study. The three-dimensional laryngeal models were established for healthy vocal folds (0 mm) and different types of sulcus vocalis with the typical depth of 1 mm, 2 mm, and 3 mm. These models with fluid-structure interaction (FSI) are computed numerically by sequential coupling method, which includes an immersed boundary method (IBM) for modelling the glottal airflow, a finite-element method (FEM) for modelling vocal fold tissue. The results show that a deeper sulcus vocalis in the cover layer decreases the vibrating frequency of vocal folds and expands the prephonatory glottal half-width which increases the phonation threshold pressure. The larger sulcus vocalis depth makes vocal folds difficult to vibrate and phonate. The effects of sulcus vocalis depth suggest that the feature such as phonation threshold pressure could assist in the detection of healthy vocal folds and different types of sulcus vocalis.

Low-dose computed tomography (LDCT) is a specialized CT scan with a lower radiation dose than normal-dose CT. However, the reduced radiation dose can introduce noise and artifacts, affecting diagnostic accuracy. To enhance the LDCT image quality, we propose a Contextual Contrast Detail Attention Feature Fusion Network (CDAF-Net) for LDCT denoising. Firstly, the LDCT image, with dimensions 1 × H × W, is mapped to a feature map with dimensions C × H × W, and it is processed through the Contextual Contrast Detail Attention (CCDA) module and the Selective Kernel Feature Fusion (SKFF) module. The CCDA module combines a global contextual attention mechanism with detail-enhanced differential convolutions to better understand the overall semantics and structure of the LDCT image, capturing subtle changes and details. The SKFF module effectively merges shallow features extracted by the encoder with deep features from the decoder, integrating feature representations from different levels. This process is repeated across four different resolution feature maps, and the denoised LDCT image is output through a skip connection. We conduct experiments on the Mayo dataset, the LDCT-and-Projection-Data dataset, and the Piglet dataset. Specifically, the CDAF-Net achieves the optimal metrics with a PSNR of 33.7262 dB, an SSIM of 0.9254, and an RMSE of 5.3731 on the Mayo dataset. Improvements are also observed in head CT and ultra-low-dose chest CT images of the LDCT-and-Projection-Data dataset and the Piglet dataset. Experimental results show that the proposed CDAF-Net algorithm provides superior denoising performance compared with the state-of-the-art (SOTA) algorithms.