Partial hard occluded target reconstruction of Fourier single pixel imaging guided through range slice.

Fourier single pixel imaging utilizes pre-programmed patterns for laser spatial distribution modulation to reconstruct intensity image of the target through reconstruction algorithms. The approach features non-locality and high anti-interference performance. However, Poor image quality is induced when the target of interest is occluded in Fourier single pixel imaging. To address the problem, a deep learning-based image inpainting algorithm is employed within Fourier single pixel imaging to reconstruct partially obscured targets with high quality. It applies a distance-based segmentation method to segment obscured regions and the target of interest. Additionally, it utilizes an image inpainting network that combines multi-scale sparse convolution and transformer architecture, along with a reconstruction network that integrates Channel Attention Mechanism and Attention Gate modules to reconstruct complete and clear intensity images of the target of interest. The proposed method significantly expands the application scenarios and improves the imaging quality of Fourier single pixel imaging. Simulation and real-world experimental results demonstrate that the proposed method exhibits the high inpainting and reconstruction capacity in the conditions of hard occlusion and down-sampling.

Optimization of an Ultrasound-Assisted Extraction Technique and the Effectiveness of the Sunscreen Components Isolated from Bletilla striata.

Bletilla striata is the dried tuber of B. striata (Thund.) Reichb.f., which has antibacterial, anti-inflammatory, anti-tumor, antioxidant and wound healing effects. Traditionally, it has been used for hemostasis therapy, as well as to treat sores, swelling and chapped skin. In this study, we used the ultraviolet (UV) absorbance rate of B. striata extracts as the index, and the extraction was varied with respect to the solid-liquid ratio, ethanol concentration, ultrasonic time and temperature in order to optimize the extraction process for its sunscreen components. The main compounds in the sunscreen ingredients of Baiji (B. striata) were analyzed using ultra-high-performance liquid chromatography combined with quadrupole time-of-flight tandem mass spectrometry. The sunscreen properties were subsequently evaluated in vitro using the 3M tape method. The results show that the optimal extraction conditions for the sunscreen components of B. striata were a solid-liquid ratio of 1:40 (g/mL), an ethanol concentration of 50%, an ultrasonic time of 50 min and a temperature of 60 °C. A power of 100 W and an ultrasonic frequency of 40 Hz were used throughout the experiments. Under these optimized conditions, the UV absorption rate of the isolated sunscreen components in the UVB region reached 84.38%, and the RSD was 0.11%. Eighteen compounds were identified, including eleven 2-isobutyl malic acid glucose oxybenzyl esters, four phenanthrenes, two bibenzyl and one α-isobutylmalic acid. An evaluation of the sunscreen properties showed that the average UVB absorption values for the sunscreen samples from different batches of B. striata ranged from 0.727 to 1.201. The sunscreen ingredients of the extracts from B. striata had a good UV absorption capacity in the UVB area, and they were effective in their sunscreen effects under medium-intensity sunlight. Therefore, this study will be an experimental reference for the extraction of sunscreen ingredients from the B. striata plant, and it provides evidence for the future development of B. striata as a candidate cosmetic raw material with UVB protection properties.

PE-RASP: range image stitching of photon-efficient imaging through reconstruction, alignment, stitching integration network based on intensity image priors.

Single photon imaging integrates advanced single photon detection technology with Laser Radar (LiDAR) technology, offering heightened sensitivity and precise time measurement. This approach finds extensive applications in biological imaging, remote sensing, and non-visual field imaging. Nevertheless, current single photon LiDAR systems encounter challenges such as low spatial resolution and a limited field of view in their intensity and range images due to constraints in the imaging detector hardware. To overcome these challenges, this study introduces a novel deep learning image stitching algorithm tailored for single photon imaging. Leveraging the robust feature extraction capabilities of neural networks and the richer feature information present in intensity images, the algorithm stitches range images based on intensity image priors. This innovative approach significantly enhances the spatial resolution and imaging range of single photon LiDAR systems. Simulation and experimental results demonstrate the effectiveness of the proposed method in generating high-quality stitched single-photon intensity images, and the range images exhibit comparable high quality when stitched with prior information from the intensity images.

An incremental anomaly detection model for virtual machines.

Self-Organizing Map (SOM) algorithm as an unsupervised learning method has been applied in anomaly detection due to its capabilities of self-organizing and automatic anomaly prediction. However, because of the algorithm is initialized in random, it takes a long time to train a detection model. Besides, the Cloud platforms with large scale virtual machines are prone to performance anomalies due to their high dynamic and resource sharing characters, which makes the algorithm present a low accuracy and a low scalability. To address these problems, an Improved Incremental Self-Organizing Map (IISOM) model is proposed for anomaly detection of virtual machines. In this model, a heuristic-based initialization algorithm and a Weighted Euclidean Distance (WED) algorithm are introduced into SOM to speed up the training process and improve model quality. Meanwhile, a neighborhood-based searching algorithm is presented to accelerate the detection time by taking into account the large scale and high dynamic features of virtual machines on cloud platform. To demonstrate the effectiveness, experiments on a common benchmark KDD Cup dataset and a real dataset have been performed. Results suggest that IISOM has advantages in accuracy and convergence velocity of anomaly detection for virtual machines on cloud platform.