Semantic segmentation of the sensed point cloud data plays a significant role in scene understanding and reconstruction, robot navigation, etc. This work presents a Graph Convolutional Network integrating K-Nearest Neighbor searching (KNN) and Vector of Locally Aggregated Descriptors (VLAD). KNN searching is utilized to construct the topological graph of each point and its neighbors. Then, we perform convolution on the edges of constructed graph to extract representative local features by multiple Multilayer Perceptions (MLPs). Afterwards, a trainable VLAD layer, NetVLAD, is embedded in the feature encoder to aggregate the local and global contextual features. The designed feature encoder is repeated for multiple times, and the extracted features are concatenated in a jump-connection style to strengthen the distinctiveness of features and thereby improve the segmentation. Experimental results on two datasets show that the proposed work settles the shortcoming of insufficient local feature extraction and promotes the accuracy (mIoU 60.9% and oAcc 87.4% for S3DIS) of semantic segmentation comparing to existing models.

This study focuses on fast and robust outlier matches removal strategy to improve the efficiency and precision of initial alignment and further the quality of pairwise registration. Starts from the point matches obtained via feature detecting and matching, the distance disparity matrix derived from Euclidean invariants of rigid transformation is introduced, based on which a fast and effective pruning method is proposed to eliminate the outlier correspondences, especially the sharp ones. Then, the remaining matches are sent into the enhanced least‐square backward method to estimate an initial transformation in lesser attempts. Since most of the outliers are rejected, presented backward method could provide a finer alignment to input point clouds in higher efficiency than existing methods, and the following refining procedure converges to a more precise registration consuming fewer iterations, which have been proved in designed experiments. The thresholds employed in the pipeline are all automatically determined according to the actual resolution of input point clouds. Users are just required to control the error precision through a scale factor, in which way the inaccuracy and inconvenience of manually threshold defining are avoided.

Commercial off-the-shelf (COTS) field-programmable gate arrays (FPGAs) with a 28-nm process have become popular devices for computing systems. Although current generation FPGAs have advantages over previous models, the phenomenon of circuit aging has become more significant with the sharp reduction in the process size of FPGAs. Aging results in FPGA performance degradation over time and, ultimately, hard faults. However, few studies have focused on understanding aging mechanisms or estimating the aging trend of 28-nm FPGAs. For this, we used a ring oscillator (RO)-based test structure to extract data and build a dataset that could be used to predict aging trends and determine the primary aging mechanisms of 28-nm FPGAs. Moreover, we proposed a correction method to correct temperature-induced measurement errors in accelerated tests. Furthermore, we employed four machine learning (ML) technologies that were based on accurate measurement datasets to predict FPGA aging trends. In the experiment, 24 XILINX 7-series FPGAs (28 nm) were evaluated for 10+ years of circuit operation using accelerated tests. The results showed that the aging effects of negative-bias temperature instability (NBTI) was the primary aging mechanism. The correction method proposed in this paper could effectively eliminate measurement errors. In addition, the minimum prediction error rate of the ML model was only 0.292%.

Anonymous access is an effective method for preserving privacy in access control. This study assumes that anonymous access control requires both frameworks and policies. Numerous solutions have been proposed for anonymous access at the framework level. In this study, these solutions are analyzed and quantified using a unified attribute-based access control (ABAC) anonymous access reference framework. Anonymous access at the framework level is the first line of defense, and inappropriate policies may undermine subject anonymity. An anonymity metric is proposed at the policy level to prevent authorization authority from re-identification using specific attributes and policies. The anonymity metric evaluates the risk of re-identifying a subject due to inappropriate access requests, as well as subject attribute assignment schemes and policies. This study is the first to focus on anonymity at the policy level in ABAC. Furthermore, a formal definition of anonymity suitable for ABAC is proposed. The feasibility of the proposed anonymity metric is verified through simulations.