Effects of different ground segmentation methods on the accuracy of UAV-based canopy volume measurements.

The nonuniform distribution of fruit tree canopies in space poses a challenge for precision management. In recent years, with the development of Structure from Motion (SFM) technology, unmanned aerial vehicle (UAV) remote sensing has been widely used to measure canopy features in orchards to balance efficiency and accuracy. A pipeline of canopy volume measurement based on UAV remote sensing was developed, in which RGB and digital surface model (DSM) orthophotos were constructed from captured RGB images, and then the canopy was segmented using U-Net, OTSU, and RANSAC methods, and the volume was calculated. The accuracy of the segmentation and the canopy volume measurement were compared. The results show that the U-Net trained with RGB and DSM achieves the best accuracy in the segmentation task, with mean intersection of concatenation (MIoU) of 84.75% and mean pixel accuracy (MPA) of 92.58%. However, in the canopy volume estimation task, the U-Net trained with DSM only achieved the best accuracy with Root mean square error (RMSE) of 0.410 m3, relative root mean square error (rRMSE) of 6.40%, and mean absolute percentage error (MAPE) of 4.74%. The deep learning-based segmentation method achieved higher accuracy in both the segmentation task and the canopy volume measurement task. For canopy volumes up to 7.50 m3, OTSU and RANSAC achieve an RMSE of 0.521 m3 and 0.580 m3, respectively. Therefore, in the case of manually labeled datasets, the use of U-Net to segment the canopy region can achieve higher accuracy of canopy volume measurement. If it is difficult to cover the cost of data labeling, ground segmentation using partitioned OTSU can yield more accurate canopy volumes than RANSAC.

Wide field-of-view laser-based white light transmitter for visible light communications.

The advancement demands of high-speed wireless data link ask for higher requirements on visible light communication (VLC), where wide coverage stands as a critical criterion. Here, we present the design and implementation of a transmitter structure capable of emitting a high-power wide-coverage white light laser. This laser source exhibits excellent stability, with an irradiation range extending to a half-angle of 20°. Its high brightness satisfies the needs of indoor illumination while maintaining excellent communication performance. Utilizing bit-loading discrete multi-tone modulation, a peak data transmission rate of 3.24 Gbps has been achieved, spanning 1 to 5 m. Remarkably, the data rates exceed 2.5 Gbps within a 40° range at a distance of 5 m, enabling a long-distance, wide coverage, high-speed VLC link for future mobile network applications.

Laser-Based Mobile Visible Light Communication System.

Mobile visible light communication (VLC) is key for integrating lighting and communication applications in the 6G era, yet there exists a notable gap in experimental research on mobile VLC. In this study, we introduce a mobile VLC system and investigate the impact of mobility speed on communication performance. Leveraging a laser-based light transmitter with a wide coverage, we enable a light fidelity (LiFi) system with a mobile receiving end. The system is capable of supporting distances from 1 m to 4 m without a lens and could maintain a transmission rate of 500 Mbps. The transmission is stable at distances of 1 m and 2 m, but an increase in distance and speed introduces interference to the system, leading to a rise in the Bit Error Rate (BER). The mobile VLC experimental system provides a viable solution to the issue of mobile access in the integration of lighting and communication applications, establishing a solid practical foundation for future research.

Titanium Dioxide and N-Doped Carbon Hybrid Nanofiber Modulated Ru Nanoclusters for High-Efficient Hydrogen Evolution Reaction Electrocatalyst.

The designing and fabricating highly active hydrogen evolution reaction (HER) electrocatalysts that can superior to Pt/C is extremely desirable but challenging. Herein, the fabrication of Ru/TiO2/N-doped carbon (Ru/TiO2/NC) nanofiber is reported as a novel and highly active HER electrocatalyst through electrospinning and subsequent pyrolysis treatment, in which Ru nanoclusters are dispersed into TiO2/NC hybrid nanofiber. As a novel support, experimental and theoretical calculation results reveal that TiO2/NC can more effectively accelerate water dissociation as well as optimize the adsorption strength of *H than TiO2 and NC, thus leading to a significantly enhanced HER activity, which merely requires an overpotential of 18 mV to reach 10 mA cm-2, outperforming Pt/C in an alkaline solution. The electrolytic cell composed of Ru/TiO2/NC nanofiber and NiFe LDH/NF can generate 500 and 1000 mA cm-2 at voltages of 1.631 and 1.753 V, respectively. Furthermore, the electrolytic cell also exhibits remarkable durability for at least 100 h at 200 mA cm-2 with negligible degradation in activity. The present work affords a deep insight into the influence of support on the activity of electrocatalyst and the strategy proposed in this research can also be extended to fabricate various other types of electrocatalysts for diverse electrocatalytic applications.

Severity-stratification of interstitial lung disease by deep learning enabled assessment and quantification of lesion indicators from HRCT images.

BACKGROUND: Interstitial lung disease (ILD) represents a group of chronic heterogeneous diseases, and current clinical practice in assessment of ILD severity and progression mainly rely on the radiologist-based visual screening, which greatly restricts the accuracy of disease assessment due to the high inter- and intra-subjective observer variability. OBJECTIVE: To solve these problems, in this work, we propose a deep learning driven framework that can assess and quantify lesion indicators and outcome the prediction of severity of ILD. METHODS: In detail, we first present a convolutional neural network that can segment and quantify five types of lesions including HC, RO, GGO, CONS, and EMPH from HRCT of ILD patients, and then we conduct quantitative analysis to select the features related to ILD based on the segmented lesions and clinical data. Finally, a multivariate prediction model based on nomogram to predict the severity of ILD is established by combining multiple typical lesions. RESULTS: Experimental results showed that three lesions of HC, RO, and GGO could accurately predict ILD staging independently or combined with other HRCT features. Based on the HRCT, the used multivariate model can achieve the highest AUC value of 0.755 for HC, and the lowest AUC value of 0.701 for RO in stage I, and obtain the highest AUC value of 0.803 for HC, and the lowest AUC value of 0.733 for RO in stage II. Additionally, our ILD scoring model could achieve an average accuracy of 0.812 (0.736 - 0.888) in predicting the severity of ILD via cross-validation. CONCLUSIONS: In summary, our proposed method provides effective segmentation of ILD lesions by a comprehensive deep-learning approach and confirms its potential effectiveness in improving diagnostic accuracy for clinicians.