Guorong Yu

Mobile edge computing (MEC) has emerged as a powerful solution for handling computation-intensive applications at the network edge. Task offloading has become a widely adopted strategy to optimize network resource utilization and deliver enhanced services to user equipment (UE). To meet stringent end-to-end delay and energy consumption requirements, users can offload tasks to edge servers where relevant services are pre-cached. However, the limited storage capacity and the heterogeneity of edge nodes make collaborative cache decision-making and computing resource allocation at the edge critical challenges. We address the optimization of task offloading, resource allocation, service caching, and push decisions within the MEC system through the coordinated efforts of edge servers. The objective is to minimize long-term offloading latency while maximizing the cache hit ratio, presenting a mixed-integer nonlinear programming (MINLP) problem. To tackle this challenge, we propose a novel soft actor-critic approach, transforming the problem into a Markov decision process (MDP). Numerical results demonstrate that our algorithm significantly outperforms existing methods in terms of system cost, average delay, and cache hit ratio.

Multi-modal remote sensing image (MRSI) matching suffers from severe nonlinear radiometric distortions and geometric deformations, and conventional feature-based techniques are generally ineffective. This study proposes a novel and robust MRSI matching method using the side window filter (MSWF). First, a novel side window scale space is constructed based on the side window filter (SWF), which can preserve shared image contours and facilitate the extraction of feature points within this newly defined scale space. Second, noise thresholds in phase congruency (PC) computation are adaptively refined with the Weibull distribution; weighted phase features are then exploited to determine the principal orientation of each point, from which a maximum index map (MIM) descriptor is constructed. Third, coarse position, orientation, and scale information obtained through global matching are employed to estimate image-pair geometry, after which descriptors are recalculated for precise correspondence search. MSWF is benchmarked against eight state-of-the-art multi-modal methods—six hand-crafted (PSO-SIFT, LGHD, RIFT, RIFT2, HAPCG, COFSM) and two learning-based (CMM-Net, RedFeat) methods—on three public datasets. Experiments demonstrate that MSWF consistently achieves the highest number of correct matches (NCM) and the highest rate of correct matches (RCM) while delivering the lowest root mean square error (RMSE), confirming its superiority for challenging MRSI registration tasks.

To address the matching challenge between the High Resolution Imaging Science Experiment (HiRISE) Digital Elevation Model (DEM) and the Mars Orbiter Laser Altimeter (MOLA) DEM, we propose a terrain matching framework based on the combination of point cloud coarse alignment and fine alignment methods. Firstly, we achieved global coarse localization of the HiRISE DEM through nearest neighbor matching of key Intrinsic Shape Signatures (ISS) points in the Fast Point Feature Histograms (FPFH) feature space. We introduced a graph matching strategy to mitigate gross errors in feature matching, employing a numerical method of non-cooperative game theory to solve the extremal optimization problem under Karush–Kuhn–Tucker (KKT) conditions. Secondly, to handle the substantial resolution disparities between the MOLA DEM and HiRISE DEM, we devised a smoothing weighting method tailored to enhance the Voxelized Generalized Iterative Closest Point (VGICP) approach for fine terrain registration. This involves leveraging the Euclidean distance between distributions to effectively weight loss and covariance, thereby reducing the results’ sensitivity to voxel radius selection. Our experiments show that the proposed algorithm improves the accuracy of terrain registration on the proposed Curiosity landing area’s, Mawrth Vallis, data by nearly 20%, with faster convergence and better algorithm robustness.

The traditional method for extracting the heights of urban buildings involves utilizing dense matching algorithms on stereo images to generate a digital surface model (DSM). However, for urban buildings, the disparity discontinuity issue that troubles the dense matching algorithm makes the elevations of high-rise buildings and the surrounding areas inaccurate. The occlusion caused by trees in greenbelts makes it difficult to accurately extract the ground elevation around the building. To tackle these problems, a method for building height extraction from Gaofen-7 (GF-7) stereo images enhanced by contour matching is presented. Firstly, a contour matching algorithm was proposed to extract accurate building roof elevation from GF-7 images. Secondly, a ground filtering algorithm was employed on the DSM to generate a digital elevation model (DEM), and ground elevation can be extracted from this DEM. The difference between the rooftop elevation and the ground elevation represents the building height. The presented method was verified in Yingde, Guangzhou, Guangdong Province, and Xi’an, Shaanxi Province. The experimental results demonstrate that our proposed method outperforms existing methods in building height extraction concerning accuracy.

1. With the rapid development of modern technologies, the multi-sensor comprehensive observation technique has made great progress. Multimodal image matching is a crucial step in various applicatio...

Images captured by different sensors with different spectral bands cause non-linear intensity changes between image pairs. Classic feature descriptors cannot handle this problem and are prone to yielding unsatisfactory results. Inspired by the illumination and contrast invariant properties of phase congruency, here, we propose a new descriptor to tackle this problem. The proposed descriptor generation mainly involves three steps. (1) Images are convolved with a bank of log-Gabor filters with different scales and orientations. (2) A window of fixed size is selected and divided into several blocks for each keypoint, and an oriented magnitude histogram and the orientation of the minimum moment of a phase congruency-based histogram are calculated in each block. (3) These two histograms are normalized respectively and concatenated to form the proposed descriptor. Performance evaluation experiments on three datasets were carried out to validate the superiority of the proposed method. Experimental results indicated that the proposed descriptor outperformed most of the classic and state-of-art descriptors in terms of precision and recall within an acceptable computational time.