Airborne LiDAR (Light Detection And Ranging) remote sensing for individual tree-level forest inventory necessitates proper extraction of individual trees and accurate measurement of tree structural parameters. Due to the inadequate tree finding capability offered by LiDAR technology and the complex patterns of forest canopies, significant omission and commission errors occur frequently in the segmentation results. Aimed at error reduction and accuracy refinement, this paper presents a novel adaptive mean shift-based clustering scheme aided by a tree trunk detection technique to segment individual trees and estimate tree structural parameters based solely on the airborne LiDAR data. Tree trunks are detected by analyzing points’ vertical histogram to detach all potential crown points and then clustering the separated trunk points according to their horizontal mutual distances. The detected trunk information is used to adaptively calibrate the kernel bandwidth of the mean shift procedure in the fine segmentation stage by applying an original 2D (two-dimensional) estimation of individual crown diameters. Trunk detection results and LiDAR point clusters generated by the adaptive mean shift procedures serve as mutual references for final detection of individual trees. Experimental results show that a combination of adaptive mean shift clustering and detected tree trunk can provide a significant performance improvement in individual tree-level forest measurement. Compared with conventional clustering techniques, the trunk detection-aided mean shift clustering approach can detect 91.1% of the trees (“recall”) with a higher tree positioning accuracy (the mean positioning error is reduced by 33%) in a multi-layered coniferous and broad-leaved mixed forest in South China, and 93.5% of the identified trees are correct (“precision”). The tree detection brings the estimation of structural parameters for individual trees up to an accuracy level: −2.2% mean relative error and 5.8% relative RMSE (Root Mean Square Error) for tree height and 0.6% mean relative error and 21.9% relative RMSE for crown diameter, respectively.

Identifying individual trees and delineating their canopy structures from the forest point clouddataacquiredbyanairborneLiDAR(LightDetectionAndRanging)hassignificantimplications in forestry inventory. Once accurately identified, tree structural attributes such as tree height, crown diameter, canopy based height and diameter at breast height can be derived. This paper focuses on a novel computationally efficient method to adaptively calibrate the kernel bandwidth of a computational scheme based on mean shift—a non-parametric probability density-based clustering technique—to segment the 3D (three-dimensional) forest point clouds and identify individual tree crowns. The basic concept of this method is to partition the 3D space over each test plot into small vertical units (irregular columns containing 3D spatial features from one or more trees) first, by using a fixed bandwidth mean shift procedure and a small square grouping technique, and then rough estimation of crown sizes for distinct trees within a unit, based on an original 2D (two-dimensional) incremental grid projection technique, is applied to provide a basis for dynamical calibration of the kernel bandwidth for an adaptive mean shift procedure performed in each partition. The adaptive mean shift-based scheme, which incorporates our proposed bandwidth calibration method, is validated on 10 test plots of a dense, multi-layered evergreen broad-leaved forest located in South China. Experimental results reveal that this approach can work effectively and when compared to the conventional point-based approaches (e.g., region growing, k-means clustering, fixed bandwidth or multi-scale mean shift), its accuracies are relatively high: it detects 86 percent of the trees (“recall”) and 92 percent of the identified trees are correct (“precision”), showing good potential for use in the area of forest inventory.

Distributed storage systems are well-equipped to address the demands of massive data storage and rapid access in the era of big data. However, as the scale of these systems continues to expand, challenges related to data localization, performance optimization, and secure access have become increasingly prominent, making effective metadata management critically important. Redis, a memory-based NoSQL (Not only SQL) database, has emerged as an ideal choice for metadata management in distributed storage systems due to its high performance, flexible data structures, excellent scalability, and robust data persistence mechanisms. This study employs Redis as the caching layer for metadata services in distributed storage systems and develops the Redis-cluster Automatic Performance Optimizer (RAPO) to enhance performance. By implementing load balancing among master nodes and read-write separation between master and slave nodes, the tool aims to improve the metadata service performance of Redis clusters during system maintenance and in high-concurrency scenarios. To address load balancing among master nodes, the research defines the Load Balance Index (LBI) as a metric for evaluating the degree of load distribution within the cluster. Greedy and random iterative search algorithms are designed to migrate hash slots from selected nodes, thereby balancing the node loads and enhancing overall cluster performance. By optimizing with the greedy and random iterative search algorithms, the cluster’s LBI was reduced by an average of 29.36% and 25.20% respectively during random read operations and by 24.98% and 24.03% respectively during imbalanced write operations. A load threshold is defined to achieve read-write separation between master and slave nodes. Random allocation, round-robin, weighted round-robin, and smooth weighted round-robin are employed to optimize the read-write separation. Experimental results demonstrate that the weighted round-robin and smooth weighted round-robin strategies reduce read response times by an average of 27.53% and 30.75%, respectively.

Link adaptation in coherent wireless communications has sparked great research enthusiasm. However, adaptive techniques are equally important for non-coherent counterparts although relatively less attention has been dedicated to. This letter studies the adaptive modulation for the energy detection (ED)-based non-coherent systems. Specifically, we consider a massive single-input multiple-output (SIMO) network, where a single-antenna user communicates with a base station (BS) with tens/hundreds of antennas. In particular, an optimization problem is formulated in order to find the signal-to-noise ratio (SNR) switching thresholds that maximize the average spectral efficiency (ASE). After deriving the SNR distribution and symbol error rate (SER), this problem is solved by applying the Lagrangian method. With switching thresholds, rate and power adaptation are made based on the instantaneous SNR. Numerical results indicate for the proposed scheme, there exists a trade-off between the target SER and ASE. That is, the ASE falls if one wishes to reduce the target SER. Under this circumstance, mounting more antennas can avoid the loss in ASE.