Noting the shortcomings of current methods in detecting small objects in image-based remote sensing applications, in this paper, we propose a novel implementation of single shot multibox detector (SSD) networks based on dilated convolution and feature fusion. We call this algorithm dilated convolution and feature fusion single shot multibox detector (DFSSD). This algorithm removes the random clipping steps of data preprocessing layers in conventional SSD networks and utilizes the structure of feature pyramid network (FPN) network to fuse the low-level feature map with high resolution and the high-level feature map with rich semantic information. It also enhances the receptive field of the third-level feature map of the DFSSD network by using dilated convolution. In the data processing step of the model, we use the image segmentation of the feature point region proposals to improve the training sample size. The mean average precision (mAP) value of the proposed DFSSD network, when tested on remote sensing datasets, achieves 76.51%, which is significantly higher than that of the SSD model (69.81%).

Current networking protocols deem inefficient in accommodating the two key challenges of Unmanned Aerial Vehicle (UAV) networks, namely the network connectivity loss and energy limitations. One approach to solve these issues is using learning-based routing protocols to make close-tooptimal local decisions by the network nodes, and Q-routing is a bold example of such protocols. However, the performance of the current implementations of Q-routing algorithms is not yet satisfactory, mainly due to the lack of adaptability to continued topology changes. In this paper, we propose a fullecho Q-routing algorithm with a self-adaptive learning rate that utilizes Simulated Annealing (SA) optimization to control the exploration rate of the algorithm through the temperature decline rate, which in turn is regulated by the experienced variation rate of the Q-values. Our results show that our method adapts to the network dynamicity without the need for manual re-initialization at transition points (abrupt network topology changes). Our method exhibits a reduction in the energy consumption ranging from 7% up to 82%, as well as a 2.6 fold gain in successful packet delivery rate, compared to the state of the art Q-routing protocols 1 .

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Teager energy operator (TEO), as a classic energy demodulation method, is specially employed to detect both the amplitude and frequency modulations of the vibration signals recorded from damaged elements. The fault characteristic frequency is identified in the spectrum of energy-transformed signal through the inherent amplitude demodulation. However, the TEO is easily sensitive to intensive noise and vibration interferences. To conquer this disadvantage, an alternative energy operator method, which is realized using a multiresolution symmetric difference and analytic energy operator, is proposed to cope with the vibration signals corrupted by strong noise and multiple vibration interference components. It is simple to implement bearing fault detection and provides excellent time resolution. So, the proposed energy detector is a fast and non-filtering method. The results of simulated tests and real experiments show that the enhanced energy operator can detect fault characteristics effectively in harsh working conditions and certify its superiority according to compare with previous energy operator techniques as well as some popular fault detection methods.