In this paper, we study the design of opportunistic routing for efficient data offloading in vehicular opportunistic networks with the assistance of sparsely deployed static relays. The objective is to maximize the data offloading ratio and also reduce the average delivery delay while respecting the data’s delay requirement. For this purpose, we propose a sparse relay assisted opportunistic routing algorithm for efficient data offloading. We show how to efficiently utilize the encounters with static relays for improved data offloading performance based on vehicles’ trajectories. We further design a greedy algorithm for optimized relay deployment. We reduce the computational complexity of the data offloading algorithm and also the relay deployment algorithm, respectively. Simulation results demonstrate the high performance of our proposed algorithms.

In this paper, we propose an end-to-end feature fusion at-tention network (FFA-Net) to directly restore the haze-free image. The FFA-Net architecture consists of three key components:1) A novel Feature Attention (FA) module combines Channel Attention with Pixel Attention mechanism, considering that different channel-wise features contain totally different weighted information and haze distribution is uneven on the different image pixels. FA treats different features and pixels unequally, which provides additional flexibility in dealing with different types of information, expanding the representational ability of CNNs. 2) A basic block structure consists of Local Residual Learning and Feature Attention, Local Residual Learning allowing the less important information such as thin haze region or low-frequency to be bypassed through multiple local residual connections, let main network architecture focus on more effective information. 3) An Attention-based different levels Feature Fusion (FFA) structure, the feature weights are adaptively learned from the Feature Attention (FA) module, giving more weight to important features. This structure can also retain the information of shallow layers and pass it into deep layers.The experimental results demonstrate that our proposed FFA-Net surpasses previous state-of-the-art single image dehazing methods by a very large margin both quantitatively and qualitatively, boosting the best published PSNR metric from 30.23 dB to 36.39 dB on the SOTS indoor test dataset. Code has been made available at GitHub.

Learning powerful discriminative features is a challenging task in Semi-Supervised Learning, as the estimation of the feature space is more likely to be wrong with scarcer labeled data. Previous methods utilize a relation graph with edges representing 'similarity' or 'dissimilarity' between nodes. Similar nodes are forced to output consistent features, while dissimilar nodes are forced to be inconsistent. However, since unlabeled data may be wrongly labeled, the judgment of edges may be unreliable. Besides, the nodes connected by edges may already be well fitted, thus contributing little to the model training. We propose Reliable Edge Mining (REM), which forms a reliable graph by only selecting reliable and useful edges. Guided by the graph, the feature extractor is able to learn discriminative features in a data-efficient way, and consequently boosts the accuracy of the learned classifier. Visual analyses show that the features learned are more discriminative and better reveals the underlying structure of the data. REM can be combined with perturbation-based methods like Pi-model, TempEns and Mean Teacher to further improve accuracy. Experiments prove that our method is data-efficient on simple tasks like SVHN and CIFAR-10, and achieves state-of-the-art results on the challenging CIFAR-100.

Synthetic aperture radar (SAR) images of the Earth's terrain surface contain the calculation of scattering coefficients and the simulation of backscattering echo, which are influenced by the terrain elevation and slope. In this study, we present a method of electromagnetic imaging for terrain surfaces based on airborne strip map synthetic aperture radar (SAR). First, the integral equation method was used to calculate the backscattering coefficients of terrain surfaces. Then the simulation method of backscattering echo signal is presented for the terrain surfaces from the principle of SAR imaging. Finally, the Range-Doppler Algorithm was used to obtain the imaging of terrain surface from the processing of backscattering echo signal.

Reverse k nearest neighbor (RkNN) queries have a broad application base such as decision support, profile-based marketing, and resource allocation. Previous work on RkNN search does not take textual information into consideration or limits to the Euclidean space. In the real world, however, most spatial objects are associated with textual information and lie on road networks. In this paper, we introduce a new type of queries, namely, reverse top-k Boolean spatial keyword (RkBSK) retrieval, which assumes objects are on the road network and considers both spatial and textual information. Given a data set P on a road network and a query point q with a set of keywords, an RkBSK query retrieves the points in P that have q as one of answer points for their top-k Boolean spatial keyword queries. We formalize the RkBSK query and then propose filter-and-refinement framework based algorithms for answering RkBSK search with arbitrary k and no any pre-computation. To accelerate the query process, several novel pruning heuristics that utilize both spatial and textual information are employed to shrink the search space efficiently. In addition, a new data structure called count tree has been developed to further improve query performance. A comprehensive experimental evaluation using both real and synthetic data sets demonstrates the effectiveness of our presented pruning heuristics and the performance of our proposed algorithms.