In the field of remote sensing image processing, remote sensing image object detection is a crucial undertaking. However, the existing target detection algorithms have a considerable number of model parameters, which results in a slow detection speed that is not conducive to application deployment and real-time detection. Additionally, due to the background complexity of remote sensing images and the large number of small target objects, the performance of existing algorithmic models applied directly with remote sensing images is unsatisfactory. To address the aforementioned issues, this paper proposes an efficient and lightweight architecture based on YOLOv8: AUD-YOLO. In particular, a convolution module, GEConv, based on improved efficient multi-scale attention (EMA), is proposed to replace the original ordinary convolution at the end of the backbone part. This can enhance the accuracy of target detection in remote sensing images while maintaining a lightweight operation. The utilization of dynamic upsampling operators serves to perform upsampling operations with greater accuracy and to more effectively extract image features pertaining to small target objects. Finally, the issue of sample imbalance is addressed by utilising Wise-iou as the position coordinate loss function of the neural network. In the experiments, we validate the effectiveness and robustness of the method using the public remote sensing datasets NWPU VHR-10 and DIOR. The experimental results demonstrate that the AUD-YOLO model proposed in this paper achieves mAP values of 91.1% and 82.7%, which are 1.6% and 1.5% higher than the mAP indexes of the baseline model, and the number of parameters is reduced by 4.7% compared with the original model. We comprehensively consider the number of model parameters and model detection accuracy to make the model more adept at detecting remote sensing images.

A representative skylinecontains$k$skyline points that can represent its corresponding full skyline. The existing measuring criteria of$k$representative skylines are specifically designed for static data, and they cannot effectively handle streaming data. In this paper, we focus on the problem of calculating the$k$representative skyline over data streams. First, we propose a new criterion to choose$k$skyline points as the$k$representative skyline for data stream environments, termed the$k$largest dominance skyline ($k$-LDS), which is representative to the entire data set and is highly stable over the streaming data. Second, we propose an efficient exact algorithm, called Prefix-based Algorithm (PBA), to solve the$k$-LDS problem in a 2-dimensional space. The time complexity of PBA is only$\mathcal {O}((M-k)\times k)$where$M$is the size of the full skyline set. Third, the$k$-LDS problem for a$d$-dimensional ($d\ge 3$) space turns out to be very complex. Therefore, a greedy algorithm is designed to answer$k$-LDS queries. To further accelerate the calculation, we propose a$\epsilon$-greedy algorithm which can achieve an approximate factor of$\frac{1}{(1+\epsilon)}(1-\frac{1}{\sqrt{e}})$. Experimental results on both synthetic and real-world data show that our$k$-LDS significantly outperforms its competitors in data stream environments. Furthermore, we demonstrate that the proposed$\epsilon$-greedy algorithm can solve$k$-LDS efficiently and with a competitive accuracy.

Global skyline, as an important variant of skyline, has been widely applied in multiple criteria decision making, business planning and data mining, while there are no previous studies on the global skyline query in the subspace. Hence in this paper we propose subspace global skyline (SGS) query, which is concerned about global skyline in ad hoc subspace. Firstly, we propose an appropriate index structure RB-tree to rapidly find the initial scan positions of query. Secondly, by making analysis of basic properties of SGS, we propose a single SGS algorithm based on RB-tree (SSRB) to compute SGS points. Then an optimized single SGS algorithm based on RB-tree (OSSRB) is proposed, which can reduce the scan space and improve the computation efficiency in contrast to SSRB. Next, by sharing the scan space of different queries, a multiple SGS algorithm based on RB-tree (MSRB) is proposed to compute multiple SGS (MSGS). Finally, the performances of our proposed algorithms are verified through a large number of simulation experiments.

The state observer design problem for linear systems with delayed measurements is considered. A functional-based transformation is first presented, which transforms the system with delayed measurements into a system without delay formally. Based on the transformed system, we design full-order observer and reduced-order observer for the original delayed measurements systems respectively. Simulation results demonstrate the effectiveness of the proposed design approach.