Sparse Localization with a Mobile Beacon Based on LU Decomposition in Wireless Sensor Networks

Node localization is the core in wireless sensor network. It can be solved by powerful beacons, which are equipped with global positioning system devices to know their location information. In this article, we present a novel sparse localization approach with a mobile beacon based on LU decomposition. Our scheme firstly translates node localization problem into a 1-sparse vector recovery problem by establishing sparse localization model. Then, LU decomposition pre-processing is adopted to solve the problem that measurement matrix does not meet the re¬stricted isometry property. Later, the 1-sparse vector can be exactly recovered by compressive sensing. Finally, as the 1-sparse vector is approximate sparse, weighted Cen¬troid scheme is introduced to accurately locate the node. Simulation and analysis show that our scheme has better localization performance and lower requirement for the mobile beacon than MAP+GC, MAP-M, and MAP-M&N schemes. In addition, the obstacles and DOI have little effect on the novel scheme, and it has great localization performance under low SNR, thus, the scheme proposed is robust

LS-Net:Lightweight Segmentation Network for Dermatological Epidermal Segmentation in Optical Coherence Tomography Imaging

Optical coherence tomography (OCT) can be an important tool for non-invasivedermatological evaluation, providing useful data on epidermal integrity for diagnosing skindiseases. Despite its benefits, OCT’s utility is limited by the challenges of accurate, fastepidermal segmentation due to the skin morphological diversity. To address this, we introducea lightweight segmentation network (LS-Net), a novel deep learning model that combines therobust local feature extraction abilities of Convolution Neural Network and the long-terminformation processing capabilities of Vision Transformer. LS-Net has a depth-wiseconvolutional transformer for enhanced spatial contextualization and a squeeze-and-excitationblock for feature recalibration, ensuring precise segmentation while maintaining computationalefficiency. Our network outperforms existing methods, demonstrating high segmentationaccuracy (mean Dice: 0.9624 and mean IoU: 0.9468) with significantly reduced computationaldemands (floating point operations: 1.131 G). We further validate LS-Net on our acquireddataset, showing its effectiveness in various skin sites (e.g., face, palm) under realistic clinicalconditions. This model promises to enhance the diagnostic capabilities of OCT, making it avaluable tool for dermatological practice

Effects of Intrinsic Tannins on Metabolome During Sainfoin Ensiling

Condensed tannins (CT) from sainfoin have a high capacity to inhibit proteolysis. The objective of the present study was to investigate the effects of CT (following supplementation of deactivated CT with polyethylene glycol [PEG]) on the metabolome during sainfoin ensiling. In total, 510 metabolites were identified after 60 d of sainfoin ensiling, with 33 metabolites were annotated in the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Among those metabolites, phospholipids were the most abundant (72.7% of total 33 metabolites). In addition, 10 up-regulated and 23 down-regulated metabolites, respectively, were identified in the PEG treated group when compared with the control group, after 60 d of ensiling (p \u3c 0.05). Pediococcus (correlated with 20 metabolites, R2 \u3e 0.88, p\u3c 0.05) and Lactobacillus (correlated with 16 metabolites, R2 \u3e 0.88, p \u3c 0.05) were the bacteria most correlated with metabolites. The results suggest antagonistic effects between Lactobacillus and Pediococcus occur during ensiling. The proteolysis decreased partly due to CT inhibiting Pediococcus activity during ensiling, with Pediococcus being significantly and positively correlated with dopamine after 60 d of ensiling (R2=0.8857, p \u3c 0.05)

U-shaped fusion convolutional transformer based workflow for fast optical coherence tomography angiography generation in lips

Oral disorders, including oral cancer, pose substantial diagnostic challenges due to late-stage diagnosis, invasive biopsy procedures, and the limitations of existing non-invasive imaging techniques. Optical coherence tomography angiography (OCTA) shows potential in delivering non-invasive, real-time, high-resolution vasculature images. However, the quality of OCTA images are often compromised due to motion artifacts and noise, necessitating more robust and reliable image reconstruction approaches. To address these issues, we propose a novel model, a U-shaped fusion convolutional transformer (UFCT), for the reconstruction of high-quality, low-noise OCTA images from two-repeated OCT scans. UFCT integrates the strengths of convolutional neural networks (CNNs) and transformers, proficiently capturing both local and global image features. According to the qualitative and quantitative analysis in normal and pathological conditions, the performance of the proposed pipeline outperforms that of the traditional OCTA generation methods when only two repeated B-scans are performed. We further provide a comparative study with various CNN and transformer models and conduct ablation studies to validate the effectiveness of our proposed strategies. Based on the results, the UFCT model holds the potential to significantly enhance clinical workflow in oral medicine by facilitating early detection, reducing the need for invasive procedures, and improving overall patient outcomes.</p

U-shaped fusion convolutional transformer based workflow for fast optical coherence tomography angiography generation in lips

Oral disorders, including oral cancer, pose substantial diagnostic challenges due to late-stage diagnosis, invasive biopsy procedures, and the limitations of existing non-invasive imaging techniques. Optical coherence tomography angiography (OCTA) shows potential in delivering non-invasive, real-time, high-resolution vasculature images. However, the quality of OCTA images are often compromised due to motion artifacts and noise, necessitating more robust and reliable image reconstruction approaches. To address these issues, we propose a novel model, a U-shaped fusion convolutional transformer (UFCT), for the reconstruction of high-quality, low-noise OCTA images from two-repeated OCT scans. UFCT integrates the strengths of convolutional neural networks (CNNs) and transformers, proficiently capturing both local and global image features. According to the qualitative and quantitative analysis in normal and pathological conditions, the performance of the proposed pipeline outperforms that of the traditional OCTA generation methods when only two repeated B-scans are performed. We further provide a comparative study with various CNN and transformer models and conduct ablation studies to validate the effectiveness of our proposed strategies. Based on the results, the UFCT model holds the potential to significantly enhance clinical workflow in oral medicine by facilitating early detection, reducing the need for invasive procedures, and improving overall patient outcomes.</p

LS-Net:Lightweight Segmentation Network for Dermatological Epidermal Segmentation in Optical Coherence Tomography Imaging

Optical coherence tomography (OCT) can be an important tool for non-invasivedermatological evaluation, providing useful data on epidermal integrity for diagnosing skindiseases. Despite its benefits, OCT’s utility is limited by the challenges of accurate, fastepidermal segmentation due to the skin morphological diversity. To address this, we introducea lightweight segmentation network (LS-Net), a novel deep learning model that combines therobust local feature extraction abilities of Convolution Neural Network and the long-terminformation processing capabilities of Vision Transformer. LS-Net has a depth-wiseconvolutional transformer for enhanced spatial contextualization and a squeeze-and-excitationblock for feature recalibration, ensuring precise segmentation while maintaining computationalefficiency. Our network outperforms existing methods, demonstrating high segmentationaccuracy (mean Dice: 0.9624 and mean IoU: 0.9468) with significantly reduced computationaldemands (floating point operations: 1.131 G). We further validate LS-Net on our acquireddataset, showing its effectiveness in various skin sites (e.g., face, palm) under realistic clinicalconditions. This model promises to enhance the diagnostic capabilities of OCT, making it avaluable tool for dermatological practice