Remote Sensing Image Ship Matching Utilising Line Features for Resource-Limited Satellites.

The existing image matching methods for remote sensing scenes are usually based on local features. The most common local features like SIFT can be used to extract point features. However, this kind of methods may extract too many keypoints on the background, resulting in low attention to the main object in a single image, increasing resource consumption and limiting their performance. To address this issue, we propose a method that could be implemented well on resource-limited satellites for remote sensing images ship matching by leveraging line features. A keypoint extraction strategy called line feature based keypoint detection (LFKD) is designed using line features to choose and filter keypoints. It can strengthen the features at corners and edges of objects and also can significantly reduce the number of keypoints that cause false matches. We also present an end-to-end matching process dependent on a new crop patching function, which helps to reduce complexity. The matching accuracy achieved by the proposed method reaches 0.972 with only 313 M memory and 138 ms testing time. Compared to the state-of-the-art methods in remote sensing scenes in extensive experiments, our keypoint extraction method can be combined with all existing CNN models that can obtain descriptors, and also improve the matching accuracy. The results show that our method can achieve ∼50% test speed boost and ∼30% memory saving in our created dataset and public datasets.

Static resource allocation of advanced reservation requests in elastic optical networks.

Considering the flexibility characteristic of advanced reservation (AR) requests, the problem of static routing, modulation, spectrum, and time assignment (RMSTA) of AR requests in elastic optical networks is studied in this paper, in order to deploy the spectrum resource economically and enable more requests to be served. The multi-objective integer linear program (ILP) model, which can minimize the maximum utilized frequency and time slot indices as well as find a trade-off between them, is used to formulate the RMSTA problem. Then the proportion optimal RMSTA (PO-RMSTA) heuristic algorithm with three sorting strategies is proposed to get the sub-optimal solutions. The PO-RMSTA algorithm and sorting strategies, ascending order of elastic time (AET), descending order of data volume (DDV), and ascending order of alternative schemes (AAS), are simulated in our work and proved to obtain the approximate optimal solutions. The sorting policy AET achieved the best performance when minimizing the maximum utilized frequency slot index, whereas the sorting policy DDV worked best when minimizing the maximum utilized time slot index. As for the compromise between two indices, both AET and AAS provided satisfying results.

Resource Allocation in Wireless Powered IoT System: A Mean Field Stackelberg Game-Based Approach.

The IoT system has become a significant component of next generation networks, and drawn a lot of research interest in academia and industry. As the sensor nodes in the IoT system are always battery-limited devices, the power control problem is a serious problem in the IoT system which needs to be solved. In this paper, we research the resource allocation in the wireless powered IoT system, which includes one hybrid access point (HAP) and many wireless sensor nodes, to obtain the optimal power level for information transmission and energy transfer simultaneously. The relationship between the HAP and the sensor nodes are formulated as the Stackelberg game, and the dynamic variations of the energy for both the HAP and IoT devices are formulated through the dynamic game with mean field control. Then the power control in the wireless powered IoT system is formulated as a mean field Stackelberg game model. We aim to minimize the transmission cost for each sensor node based on optimally power resource allocation. Meanwhile, we attempt to minimize the energy transfer cost based on power control. As a result, the optimal solutions based on the mean field control of the sensor nodes and the HAP are achieved through dynamic programming theory and the law of large numbers, and ε -Nash equilibriums can be obtained. The energy variations for both the sensor nodes and HAP after the control of resource allocation based on the proposed approach are verified based on the simulation results.