Topological charge identification of superimposed orbital angular momentum beams under turbulence using an attention mechanism.

Due to the unique features, orbital angular momentum (OAM) beams have been widely explored for different applications. Accurate determination of the topological charge (TC) of these beams is crucial for their optimal utilization. In this paper, we propose a method that combines adaptive image processing techniques with a simple, parameter-free attention module (SimAM) based convolutional neural network to accurately identify the TC of high-order superimposed OAM beams. Experimental results demonstrate that under the combined influence of non-extreme light intensity and turbulence, it can achieve >95% identification accuracy of TCs ranging from ±1 to ±40. Moreover, even under partial-pattern-missing conditions, our method maintains an accuracy rate of over 80%. Compared with traditional attention mechanisms, SimAM does not require additional network design, significantly reducing the computational costs. Our approach showcases remarkable efficiency, robustness, and cost-effectiveness, making it adaptable to challenging factors such as non-uniform lighting and partially occluded light paths. This research provides a new direction for recognizing OAM modes with valuable implications for the future of communication systems.

Visible-Light-Induced Diselenide-Crosslinked Polymeric Micelles for ROS-Triggered Drug Delivery.

To synthesize an effective and versatile nano-platform serving as a promising carrier for controlled drug delivery, visible-light-induced diselenide-crosslinked polyurethane micelles were designed and prepared for ROS-triggered on-demand doxorubicin (DOX) release. A rationally designed amphiphilic block copolymer, poly(ethylene glycol)-b-poly(diselenolane diol-co-isophorone diisocyanate)-b-poly(ethylene glycol) (PEG-b-PUSe-b-PEG), which incorporates dangling diselenolane groups within the hydrophobic PU segments, was initially synthesized through the polycondensation reaction. In aqueous media, this type of amphiphilic block copolymer can self-assemble into micellar aggregates and encapsulate DOX within the micellar core, forming DOX-loaded micelles that are subsequently in situ core-crosslinked by diselenides via a visible-light-triggered metathesis reaction of Se-Se bonds. Compared with the non-crosslinked micelles (NCLMs), the as-prepared diselenide-crosslinked micelles (CLMs) exhibited a smaller particle size and improved colloidal stability. In vitro release studies have demonstrated suppressed drug release behavior for CLMs in physiological conditions, as compared to the NCLMs, whereas a burst release of DOX occurred upon exposure to an oxidation environment. Moreover, MTT assay results have revealed that the crosslinked polyurethane micelles displayed no significant cytotoxicity towards HeLa cells. Cellular uptake analyses have suggested the effective internalization of DOX-loaded crosslinked micelles and DOX release within cancer cells. These findings suggest that this kind of ROS-triggered reversibly crosslinked polyurethane micelles hold significant potential as a ROS-responsive drug delivery system.

Image-fusion-based object detection using a time-of-flight camera.

In this work, we demonstrate an innovative object detection framework based on depth and active infrared intensity images fusion with a time-of-flight (ToF) camera. A slide window weight fusion (SWWF) method provides fuse image with two modalities to localize targets. Then, the depth and intensity information is extracted to construct a joint feature space. Next, we utilize four machine learning methods to achieve object recognition. To verify this method, experiments are performed on an in-house dataset containing 1066 images, which are categorized into six different surface materials. Consequently, the approach performs well on localization with a 0.778 intersection over union (IoU). The best classification results are obtained with K-Nearest Neighbor (KNN) with a 98.01% total accuracy. Furthermore, our demonstrated method is less affected by various illumination conditions.

Face Mask Identification Using Spatial and Frequency Features in Depth Image from Time-of-Flight Camera.

Face masks can effectively prevent the spread of viruses. It is necessary to determine the wearing condition of masks in various locations, such as traffic stations, hospitals, and other places with a risk of infection. Therefore, achieving fast and accurate identification in different application scenarios is an urgent problem to be solved. Contactless mask recognition can avoid the waste of human resources and the risk of exposure. We propose a novel method for face mask recognition, which is demonstrated using the spatial and frequency features from the 3D information. A ToF camera with a simple system and robust data are used to capture the depth images. The facial contour of the depth image is extracted accurately by the designed method, which can reduce the dimension of the depth data to improve the recognition speed. Additionally, the classification process is further divided into two parts. The wearing condition of the mask is first identified by features extracted from the facial contour. The types of masks are then classified by new features extracted from the spatial and frequency curves. With appropriate thresholds and a voting method, the total recall accuracy of the proposed algorithm can achieve 96.21%. Especially, the recall accuracy for images without mask can reach 99.21%.

A Review: Point Cloud-Based 3D Human Joints Estimation.

Joint estimation of the human body is suitable for many fields such as human-computer interaction, autonomous driving, video analysis and virtual reality. Although many depth-based researches have been classified and generalized in previous review or survey papers, the point cloud-based pose estimation of human body is still difficult due to the disorder and rotation invariance of the point cloud. In this review, we summarize the recent development on the point cloud-based pose estimation of the human body. The existing works are divided into three categories based on their working principles, including template-based method, feature-based method and machine learning-based method. Especially, the significant works are highlighted with a detailed introduction to analyze their characteristics and limitations. The widely used datasets in the field are summarized, and quantitative comparisons are provided for the representative methods. Moreover, this review helps further understand the pertinent applications in many frontier research directions. Finally, we conclude the challenges involved and problems to be solved in future researches.

A Scaffold-based Deep Generative Model Considering Molecular Stereochemical Information.

Designing molecules with specific scaffolds can facilitate the discovery and optimization of lead compounds. Some scaffold-based molecular generation models have been developed using deep-learning methods based on specific scaffolds, although incorporating scaffold generalization is expected to achieve scaffold hopping. Moreover, most of the existing models focus on the 2D shape of the scaffold and overlook the stereochemical properties of the compound, especially for natural products. In this study, we optimized the scaffold-based molecular generation model designed by Lim et al. (Chemical Science 2020, 11, 1153-1164). Real-time ultrafast shape recognition with pharmacophore constraints (USRCAT) was introduced into the model to search for molecules similar to the 3D conformation and pharmacophore of the input scaffold sourced from the training set; the searched molecules were then used as new scaffolds to execute scaffold hopping. The optimized model could generate new molecules with the same chirality as the input scaffold. Furthermore, the probability distribution of the molecular structure and various physicochemical properties were analyzed to evaluate the model's generation capability. We thus believe that the optimized model can provide a basis for medicinal chemists to explore a wider chemical space toward optimization of the lead compounds and to screen the virtual compound library.