With the advancement of blockchain technology, hundreds of cryptocurrencies have been deployed. The bloom of heterogeneous blockchain platforms brings a new emerging problem: typically, various blockchains are isolated systems, how to securely identify and/or transfer digital properties across blockchains? There are three main kinds of cross-chain approaches: sidechains/relays, notaries, and hashed time-lock contracts. Among them, notary-based cross-chain solutions have the best compatibility and user-friendliness, but they are typically centralized. To resolve this issue, we present Bool Network – an open, distributed, secure cross-chain notary platform powered by MPC-based distributed key management over evolving hidden committees. More specifically, to protect the identities of the committee members, we propose a Ring verifiable random function (Ring VRF) protocol, where the real public key of a VRF instance can be hidden among a ring, which may be of independent interest to other cryptographic protocols. Furthermore, all the key management procedures are executed in the TEE, such as Intel SGX, to ensure the privacy and integrity of partial key components. A prototype of the proposed Bool Network is implemented in Rust language, using Polkadot Substrate.

Point cloud classification is a crucial procedure in ground scene interpretation. In this letter, density-adaptive geometric features are proposed for the classification of terrestrial laser scanning data, with the problem brought by point density variation as one of the main concerns. For each point, point spacing is estimated, respectively, based upon the distance to scanner position and the angular resolution, and then used as neighborhood scale basis to generate the search range of optimal radius. In feature extraction, we modify some common geometric features to adapt to density variation, e.g., a polar projection grid is proposed to generate projection features instead of commonly used rectangular grid. The polar grid can make sure similar number of laser beams passing through each grid. An evaluation involving five classifiers is carried out in an outdoor scene captured by Reigl-VZ400 scanner and the results show density-adaptive features have better and more stable performances than features without considering density variation, with the highest overall accuracy of 95.95%. Moreover, the proposed features perform well on the recognition of buildings from a large distance (more than 300 m in this letter).

Terrestrial laser scanning (TLS) is an important technique for tree stem detection. In this paper, a point-based method for stem detection is proposed using single-scan TLS data. One of the main concerns is the point density, which decreases rapidly with the increasing distance to the scanner position. In the proposed method, the search radius is generated adaptively, based on the relationship between the distance and point density, to make sure that the neighborhood maintains a similar scale to the corresponding point density. The belonging of each point is recognized with cuckoo search-based support vector machine, and the points labeled as stem are then clustered and filtered for further verification. The threshold for the small cluster filtering is also adaptive to deal with the problem of the cluster point number decreasing as a function of distance. The stem position is calculated with the lowest cylinder from the cluster segmentation and modeling for the stem mapping. Experiments were carried out on two plots with radii of more than 130 m. The overall detection rate was 76.1%, and 75% of the stems outside 80 m were detected with the adaptive radius, despite the point density being less than 5 cm.

This letter proposes a novel approach using vector roads to aid in generating seams for aerial image mosaicking. A representative seam of two adjacent images is extracted as follows. First, the vector roads in the overlapping area of adjacent images are overlaid with the straight skeleton of the overlapping area to build a weighted graph G(V, E). Dijkstra's algorithm is then applied to And the lowest cost path in G(V, E) that connects two intersections of the boundary polygons of adjacent images. The lowest cost path is considered as a seam candidate. Second, the seam candidate is refined by considering its surrounding pixels. The refined seam is employed as the final seam. Experiments demonstrate that this vector-based approach is typically more efficient than the existing raster-based approaches, particularly when vector road networks are available.