IEEE Access Special Section Editorial: Secure Modulations for Future Wireless Communications and Mobile Networks

Security has become an extremely important research topic in wireless networks over the last decade, as it is intimately related to both individual privacy and national security. Directional modulation, as a conventional type of secure modulations, transmits confidential information along the desired directions of legitimate receivers, and artificial noise in other directions, to deliberately confuse eavesdroppers in line-of-sight channels. Recently, artificial noise is also introduced into spatial modulation, leading to a secure spatial modulation strategy. In this Special Section in IEEE A CCESS, secure modulation is defined broadly as any secure modulation method, which includes, but is not limited to, secure directional modulation, secure spatial modulation, and secure index modulation

A robust phase extraction method for overcoming spectrum overlapping in shearography

The advantage of spatial phase-shifting shearography is its ability to extract the phase from a single speckle pattern; however, it often faces spectrum overlapping, which seriously affects phase quality. In this paper, we propose a shearography phase-extraction method based on windowed Fourier ridges, which can effectively extract phase information even in the presence of severe spectrum overlapping. A simple and efficient method was applied to determine the parameters of the windowed Fourier ridges, and a linear variation window was used to match the phase-extraction requirements for different frequency coordinates. A numerical simulation was quantitatively conducted to compare the phase-extraction results of the proposed method with those of the conventional method for various cases, and a shearography system was built with two types of objects to demonstrate the feasibility of the proposed method

Recent advances in the extraction, purification, structural-property correlations, and antiobesity mechanism of traditional Chinese medicine-derived polysaccharides: a review

Traditional Chinese medicine (TCM) has displayed preventive and therapeutic effects on many complex diseases. As natural biological macromolecules, TCM-derived antiobesogenic polysaccharides (TCMPOs) exhibit notable weight-loss effects and are seen to be a viable tactic in the fight against obesity. Current studies demonstrate that the antiobesity activity of TCMPOs is closely related to their structural characteristics, which could be affected by the extraction and purification methods. Therefore, the extraction, purification and structural-property correlations of TCMPOs were discussed. Investigation of the antiobesity mechanism of TCMPOs is also essential for their improved application. Herein, the possible antiobesity mechanisms of TCMPOs are systematically summarized: (1) modulation of appetite and satiety effects, (2) suppression of fat absorption and synthesis, (3) alteration of the gut microbiota and their metabolites, and (4) protection of intestinal barriers. This collated information could provide some insights and offer a new therapeutic approach for the management and prevention of obesity

Three-Branch BERT-Based Text Classification Network for Gastroscopy Diagnosis Text

During a hospital visit, a significant volume of Gastroscopy Diagnostic Text (GDT) data are produced, representing the unstructured gastric medical records of patients undergoing gastroscopy. As such, GDTs play a crucial role in evaluating the patient’s health, shaping treatment plans, and scheduling follow-up visits. However, given the free-text nature of GDTs, which lack a formal structure, physicians often find it challenging to extract meaningful insights from them. Furthermore, while deep learning has made significant strides in the medical domain, to our knowledge, there are not any readily available text-based pre-trained models tailored for GDT classification and analysis. To address this gap, we introduce a Bidirectional Encoder Representations from Transformers (BERT) based three-branch classification network tailored for GDTs. We leverage the robust representation capabilities of the BERT pre-trained model to deeply encode the texts. A unique three-branch decoder structure is employed to pinpoint lesion sites and determine cancer stages. Experimental outcomes validate the efficacy of our approach in GDT classification, with a precision of 0.993 and a recall of 0.784 in the early cancer category. In pinpointing cancer lesion sites, the weighted F1 score achieved was 0.849

GazeNeRF: 3D-Aware Gaze Redirection with Neural Radiance Fields

We propose GazeNeRF, a 3D-aware method for the task of gaze redirection. Existing gaze redirection methods operate on 2D images and struggle to generate 3D consistent results. Instead, we build on the intuition that the face region and eyeballs are separate 3D structures that move in a coordinated yet independent fashion. Our method leverages recent advancements in conditional image-based neural radiance fields and proposes a two-stream architecture that predicts volumetric features for the face and eye regions separately. Rigidly transforming the eye features via a 3D rotation matrix provides fine-grained control over the desired gaze angle. The final, redirected image is then attained via differentiable volume compositing. Our experiments show that this architecture outperforms naively conditioned NeRF baselines as well as previous state-of-the-art 2D gaze redirection methods in terms of redirection accuracy and identity preservation